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Attention-Based Recurrent Neural Network For Automatic Behavior Laying Hen Recognition
Published 18 Jan 2024 in cs.SD, cs.AI, cs.CL, cs.LG, and eess.AS | (2401.09880v1)
Abstract: One of the interests of modern poultry farming is the vocalization of laying hens which contain very useful information on health behavior. This information is used as health and well-being indicators that help breeders better monitor laying hens, which involves early detection of problems for rapid and more effective intervention. In this work, we focus on the sound analysis for the recognition of the types of calls of the laying hens in order to propose a robust system of characterization of their behavior for a better monitoring. To do this, we first collected and annotated laying hen call signals, then designed an optimal acoustic characterization based on the combination of time and frequency domain features. We then used these features to build the multi-label classification models based on recurrent neural network to assign a semantic class to the vocalization that characterize the laying hen behavior. The results show an overall performance with our model based on the combination of time and frequency domain features that obtained the highest F1-score (F1=92.75) with a gain of 17% on the models using the frequency domain features and of 8% on the compared approaches from the litterature.
- Banhazi T, Black J (2009) Precision livestock farming: A suite of electronic systems to ensure the application of best practice management on livestock farms. Australian Journal of Multi-Disciplinary Engineering 7(1):1–14. 10.1080/14488388.2009.11464794, URL https://doi.org/10.1080/14488388.2009.11464794, https://arxiv.org/abs/https://doi.org/10.1080/14488388.2009.11464794 Bardeli et al (2010) Bardeli R, Wolff D, Kurth F, et al (2010) Detecting bird sounds in a complex acoustic environment and application to bioacoustic monitoring. Pattern Recognition Letters 31(12):1524–1534. https://doi.org/10.1016/j.patrec.2009.09.014, URL https://www.sciencedirect.com/science/article/pii/S0167865509002487, pattern Recognition of Non-Speech Audio Bishop et al (2017) Bishop J, Falzon G, Trotter M, et al (2017) Sound analysis and detection, and the potential for precision livestock farming-a sheep vocalization case study. In: Proceedings of the 1st Asian-Australasian Conference on Precision Pastures and Livestock Farming, Hamilton-New Zealand, pp 1–7 Chelotti et al (2016) Chelotti JO, Vanrell SR, Milone DH, et al (2016) A real-time algorithm for acoustic monitoring of ingestive behavior of grazing cattle. Computers and Electronics in Agriculture 127:64–75. https://doi.org/10.1016/j.compag.2016.05.015, URL https://www.sciencedirect.com/science/article/pii/S0168169916303076 Chelotti et al (2018) Chelotti JO, Vanrell SR, Galli JR, et al (2018) A pattern recognition approach for detecting and classifying jaw movements in grazing cattle. Computers and Electronics in Agriculture 145:83–91. https://doi.org/10.1016/j.compag.2017.12.013, URL https://www.sciencedirect.com/science/article/pii/S0168169917302752 Chung et al (2013a) Chung Y, Lee J, Oh S, et al (2013a) Automatic detection of cow’s oestrus in audio surveillance system. Asian-Australasian journal of animal sciences 26(7):1030 Chung et al (2013b) Chung Y, Oh S, Lee J, et al (2013b) Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems. Sensors 13(10):12,929–12,942 Cowton et al (2018) Cowton J, Kyriazakis I, Plötz T, et al (2018) A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18(8):2521. 10.3390/s18082521, URL https://doi.org/10.3390/s18082521 Doulgerakis et al (2019) Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Bardeli R, Wolff D, Kurth F, et al (2010) Detecting bird sounds in a complex acoustic environment and application to bioacoustic monitoring. Pattern Recognition Letters 31(12):1524–1534. https://doi.org/10.1016/j.patrec.2009.09.014, URL https://www.sciencedirect.com/science/article/pii/S0167865509002487, pattern Recognition of Non-Speech Audio Bishop et al (2017) Bishop J, Falzon G, Trotter M, et al (2017) Sound analysis and detection, and the potential for precision livestock farming-a sheep vocalization case study. In: Proceedings of the 1st Asian-Australasian Conference on Precision Pastures and Livestock Farming, Hamilton-New Zealand, pp 1–7 Chelotti et al (2016) Chelotti JO, Vanrell SR, Milone DH, et al (2016) A real-time algorithm for acoustic monitoring of ingestive behavior of grazing cattle. Computers and Electronics in Agriculture 127:64–75. https://doi.org/10.1016/j.compag.2016.05.015, URL https://www.sciencedirect.com/science/article/pii/S0168169916303076 Chelotti et al (2018) Chelotti JO, Vanrell SR, Galli JR, et al (2018) A pattern recognition approach for detecting and classifying jaw movements in grazing cattle. Computers and Electronics in Agriculture 145:83–91. https://doi.org/10.1016/j.compag.2017.12.013, URL https://www.sciencedirect.com/science/article/pii/S0168169917302752 Chung et al (2013a) Chung Y, Lee J, Oh S, et al (2013a) Automatic detection of cow’s oestrus in audio surveillance system. Asian-Australasian journal of animal sciences 26(7):1030 Chung et al (2013b) Chung Y, Oh S, Lee J, et al (2013b) Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems. Sensors 13(10):12,929–12,942 Cowton et al (2018) Cowton J, Kyriazakis I, Plötz T, et al (2018) A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18(8):2521. 10.3390/s18082521, URL https://doi.org/10.3390/s18082521 Doulgerakis et al (2019) Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Bishop J, Falzon G, Trotter M, et al (2017) Sound analysis and detection, and the potential for precision livestock farming-a sheep vocalization case study. In: Proceedings of the 1st Asian-Australasian Conference on Precision Pastures and Livestock Farming, Hamilton-New Zealand, pp 1–7 Chelotti et al (2016) Chelotti JO, Vanrell SR, Milone DH, et al (2016) A real-time algorithm for acoustic monitoring of ingestive behavior of grazing cattle. Computers and Electronics in Agriculture 127:64–75. https://doi.org/10.1016/j.compag.2016.05.015, URL https://www.sciencedirect.com/science/article/pii/S0168169916303076 Chelotti et al (2018) Chelotti JO, Vanrell SR, Galli JR, et al (2018) A pattern recognition approach for detecting and classifying jaw movements in grazing cattle. Computers and Electronics in Agriculture 145:83–91. https://doi.org/10.1016/j.compag.2017.12.013, URL https://www.sciencedirect.com/science/article/pii/S0168169917302752 Chung et al (2013a) Chung Y, Lee J, Oh S, et al (2013a) Automatic detection of cow’s oestrus in audio surveillance system. Asian-Australasian journal of animal sciences 26(7):1030 Chung et al (2013b) Chung Y, Oh S, Lee J, et al (2013b) Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems. Sensors 13(10):12,929–12,942 Cowton et al (2018) Cowton J, Kyriazakis I, Plötz T, et al (2018) A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18(8):2521. 10.3390/s18082521, URL https://doi.org/10.3390/s18082521 Doulgerakis et al (2019) Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Chelotti JO, Vanrell SR, Milone DH, et al (2016) A real-time algorithm for acoustic monitoring of ingestive behavior of grazing cattle. Computers and Electronics in Agriculture 127:64–75. https://doi.org/10.1016/j.compag.2016.05.015, URL https://www.sciencedirect.com/science/article/pii/S0168169916303076 Chelotti et al (2018) Chelotti JO, Vanrell SR, Galli JR, et al (2018) A pattern recognition approach for detecting and classifying jaw movements in grazing cattle. Computers and Electronics in Agriculture 145:83–91. https://doi.org/10.1016/j.compag.2017.12.013, URL https://www.sciencedirect.com/science/article/pii/S0168169917302752 Chung et al (2013a) Chung Y, Lee J, Oh S, et al (2013a) Automatic detection of cow’s oestrus in audio surveillance system. Asian-Australasian journal of animal sciences 26(7):1030 Chung et al (2013b) Chung Y, Oh S, Lee J, et al (2013b) Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems. Sensors 13(10):12,929–12,942 Cowton et al (2018) Cowton J, Kyriazakis I, Plötz T, et al (2018) A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18(8):2521. 10.3390/s18082521, URL https://doi.org/10.3390/s18082521 Doulgerakis et al (2019) Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Chelotti JO, Vanrell SR, Galli JR, et al (2018) A pattern recognition approach for detecting and classifying jaw movements in grazing cattle. Computers and Electronics in Agriculture 145:83–91. https://doi.org/10.1016/j.compag.2017.12.013, URL https://www.sciencedirect.com/science/article/pii/S0168169917302752 Chung et al (2013a) Chung Y, Lee J, Oh S, et al (2013a) Automatic detection of cow’s oestrus in audio surveillance system. Asian-Australasian journal of animal sciences 26(7):1030 Chung et al (2013b) Chung Y, Oh S, Lee J, et al (2013b) Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems. Sensors 13(10):12,929–12,942 Cowton et al (2018) Cowton J, Kyriazakis I, Plötz T, et al (2018) A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18(8):2521. 10.3390/s18082521, URL https://doi.org/10.3390/s18082521 Doulgerakis et al (2019) Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Chung Y, Lee J, Oh S, et al (2013a) Automatic detection of cow’s oestrus in audio surveillance system. Asian-Australasian journal of animal sciences 26(7):1030 Chung et al (2013b) Chung Y, Oh S, Lee J, et al (2013b) Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems. Sensors 13(10):12,929–12,942 Cowton et al (2018) Cowton J, Kyriazakis I, Plötz T, et al (2018) A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18(8):2521. 10.3390/s18082521, URL https://doi.org/10.3390/s18082521 Doulgerakis et al (2019) Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Chung Y, Oh S, Lee J, et al (2013b) Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems. Sensors 13(10):12,929–12,942 Cowton et al (2018) Cowton J, Kyriazakis I, Plötz T, et al (2018) A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18(8):2521. 10.3390/s18082521, URL https://doi.org/10.3390/s18082521 Doulgerakis et al (2019) Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Cowton J, Kyriazakis I, Plötz T, et al (2018) A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18(8):2521. 10.3390/s18082521, URL https://doi.org/10.3390/s18082521 Doulgerakis et al (2019) Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. 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In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. 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In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. 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Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. 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Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. 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In: Proceedings of the 1st Asian-Australasian Conference on Precision Pastures and Livestock Farming, Hamilton-New Zealand, pp 1–7 Chelotti et al (2016) Chelotti JO, Vanrell SR, Milone DH, et al (2016) A real-time algorithm for acoustic monitoring of ingestive behavior of grazing cattle. Computers and Electronics in Agriculture 127:64–75. https://doi.org/10.1016/j.compag.2016.05.015, URL https://www.sciencedirect.com/science/article/pii/S0168169916303076 Chelotti et al (2018) Chelotti JO, Vanrell SR, Galli JR, et al (2018) A pattern recognition approach for detecting and classifying jaw movements in grazing cattle. Computers and Electronics in Agriculture 145:83–91. https://doi.org/10.1016/j.compag.2017.12.013, URL https://www.sciencedirect.com/science/article/pii/S0168169917302752 Chung et al (2013a) Chung Y, Lee J, Oh S, et al (2013a) Automatic detection of cow’s oestrus in audio surveillance system. Asian-Australasian journal of animal sciences 26(7):1030 Chung et al (2013b) Chung Y, Oh S, Lee J, et al (2013b) Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems. Sensors 13(10):12,929–12,942 Cowton et al (2018) Cowton J, Kyriazakis I, Plötz T, et al (2018) A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18(8):2521. 10.3390/s18082521, URL https://doi.org/10.3390/s18082521 Doulgerakis et al (2019) Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Chelotti JO, Vanrell SR, Milone DH, et al (2016) A real-time algorithm for acoustic monitoring of ingestive behavior of grazing cattle. Computers and Electronics in Agriculture 127:64–75. https://doi.org/10.1016/j.compag.2016.05.015, URL https://www.sciencedirect.com/science/article/pii/S0168169916303076 Chelotti et al (2018) Chelotti JO, Vanrell SR, Galli JR, et al (2018) A pattern recognition approach for detecting and classifying jaw movements in grazing cattle. Computers and Electronics in Agriculture 145:83–91. https://doi.org/10.1016/j.compag.2017.12.013, URL https://www.sciencedirect.com/science/article/pii/S0168169917302752 Chung et al (2013a) Chung Y, Lee J, Oh S, et al (2013a) Automatic detection of cow’s oestrus in audio surveillance system. Asian-Australasian journal of animal sciences 26(7):1030 Chung et al (2013b) Chung Y, Oh S, Lee J, et al (2013b) Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems. Sensors 13(10):12,929–12,942 Cowton et al (2018) Cowton J, Kyriazakis I, Plötz T, et al (2018) A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18(8):2521. 10.3390/s18082521, URL https://doi.org/10.3390/s18082521 Doulgerakis et al (2019) Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Chelotti JO, Vanrell SR, Galli JR, et al (2018) A pattern recognition approach for detecting and classifying jaw movements in grazing cattle. Computers and Electronics in Agriculture 145:83–91. https://doi.org/10.1016/j.compag.2017.12.013, URL https://www.sciencedirect.com/science/article/pii/S0168169917302752 Chung et al (2013a) Chung Y, Lee J, Oh S, et al (2013a) Automatic detection of cow’s oestrus in audio surveillance system. Asian-Australasian journal of animal sciences 26(7):1030 Chung et al (2013b) Chung Y, Oh S, Lee J, et al (2013b) Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems. Sensors 13(10):12,929–12,942 Cowton et al (2018) Cowton J, Kyriazakis I, Plötz T, et al (2018) A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18(8):2521. 10.3390/s18082521, URL https://doi.org/10.3390/s18082521 Doulgerakis et al (2019) Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Chung Y, Lee J, Oh S, et al (2013a) Automatic detection of cow’s oestrus in audio surveillance system. Asian-Australasian journal of animal sciences 26(7):1030 Chung et al (2013b) Chung Y, Oh S, Lee J, et al (2013b) Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems. Sensors 13(10):12,929–12,942 Cowton et al (2018) Cowton J, Kyriazakis I, Plötz T, et al (2018) A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18(8):2521. 10.3390/s18082521, URL https://doi.org/10.3390/s18082521 Doulgerakis et al (2019) Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Chung Y, Oh S, Lee J, et al (2013b) Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems. Sensors 13(10):12,929–12,942 Cowton et al (2018) Cowton J, Kyriazakis I, Plötz T, et al (2018) A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18(8):2521. 10.3390/s18082521, URL https://doi.org/10.3390/s18082521 Doulgerakis et al (2019) Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Cowton J, Kyriazakis I, Plötz T, et al (2018) A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18(8):2521. 10.3390/s18082521, URL https://doi.org/10.3390/s18082521 Doulgerakis et al (2019) Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. 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In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. 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Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. 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URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Chelotti JO, Vanrell SR, Milone DH, et al (2016) A real-time algorithm for acoustic monitoring of ingestive behavior of grazing cattle. Computers and Electronics in Agriculture 127:64–75. https://doi.org/10.1016/j.compag.2016.05.015, URL https://www.sciencedirect.com/science/article/pii/S0168169916303076 Chelotti et al (2018) Chelotti JO, Vanrell SR, Galli JR, et al (2018) A pattern recognition approach for detecting and classifying jaw movements in grazing cattle. Computers and Electronics in Agriculture 145:83–91. https://doi.org/10.1016/j.compag.2017.12.013, URL https://www.sciencedirect.com/science/article/pii/S0168169917302752 Chung et al (2013a) Chung Y, Lee J, Oh S, et al (2013a) Automatic detection of cow’s oestrus in audio surveillance system. Asian-Australasian journal of animal sciences 26(7):1030 Chung et al (2013b) Chung Y, Oh S, Lee J, et al (2013b) Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems. Sensors 13(10):12,929–12,942 Cowton et al (2018) Cowton J, Kyriazakis I, Plötz T, et al (2018) A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18(8):2521. 10.3390/s18082521, URL https://doi.org/10.3390/s18082521 Doulgerakis et al (2019) Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Chelotti JO, Vanrell SR, Galli JR, et al (2018) A pattern recognition approach for detecting and classifying jaw movements in grazing cattle. Computers and Electronics in Agriculture 145:83–91. https://doi.org/10.1016/j.compag.2017.12.013, URL https://www.sciencedirect.com/science/article/pii/S0168169917302752 Chung et al (2013a) Chung Y, Lee J, Oh S, et al (2013a) Automatic detection of cow’s oestrus in audio surveillance system. Asian-Australasian journal of animal sciences 26(7):1030 Chung et al (2013b) Chung Y, Oh S, Lee J, et al (2013b) Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems. Sensors 13(10):12,929–12,942 Cowton et al (2018) Cowton J, Kyriazakis I, Plötz T, et al (2018) A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18(8):2521. 10.3390/s18082521, URL https://doi.org/10.3390/s18082521 Doulgerakis et al (2019) Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Chung Y, Lee J, Oh S, et al (2013a) Automatic detection of cow’s oestrus in audio surveillance system. Asian-Australasian journal of animal sciences 26(7):1030 Chung et al (2013b) Chung Y, Oh S, Lee J, et al (2013b) Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems. Sensors 13(10):12,929–12,942 Cowton et al (2018) Cowton J, Kyriazakis I, Plötz T, et al (2018) A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18(8):2521. 10.3390/s18082521, URL https://doi.org/10.3390/s18082521 Doulgerakis et al (2019) Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Chung Y, Oh S, Lee J, et al (2013b) Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems. Sensors 13(10):12,929–12,942 Cowton et al (2018) Cowton J, Kyriazakis I, Plötz T, et al (2018) A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18(8):2521. 10.3390/s18082521, URL https://doi.org/10.3390/s18082521 Doulgerakis et al (2019) Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Cowton J, Kyriazakis I, Plötz T, et al (2018) A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18(8):2521. 10.3390/s18082521, URL https://doi.org/10.3390/s18082521 Doulgerakis et al (2019) Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. 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In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. 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Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. 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Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. 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In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Chung Y, Lee J, Oh S, et al (2013a) Automatic detection of cow’s oestrus in audio surveillance system. Asian-Australasian journal of animal sciences 26(7):1030 Chung et al (2013b) Chung Y, Oh S, Lee J, et al (2013b) Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems. Sensors 13(10):12,929–12,942 Cowton et al (2018) Cowton J, Kyriazakis I, Plötz T, et al (2018) A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18(8):2521. 10.3390/s18082521, URL https://doi.org/10.3390/s18082521 Doulgerakis et al (2019) Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Chung Y, Oh S, Lee J, et al (2013b) Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems. Sensors 13(10):12,929–12,942 Cowton et al (2018) Cowton J, Kyriazakis I, Plötz T, et al (2018) A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18(8):2521. 10.3390/s18082521, URL https://doi.org/10.3390/s18082521 Doulgerakis et al (2019) Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Cowton J, Kyriazakis I, Plötz T, et al (2018) A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18(8):2521. 10.3390/s18082521, URL https://doi.org/10.3390/s18082521 Doulgerakis et al (2019) Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. 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In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. 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In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. 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Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. 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Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. 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Sensors 13(10):12,929–12,942 Cowton et al (2018) Cowton J, Kyriazakis I, Plötz T, et al (2018) A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18(8):2521. 10.3390/s18082521, URL https://doi.org/10.3390/s18082521 Doulgerakis et al (2019) Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. 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Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Chung Y, Oh S, Lee J, et al (2013b) Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems. Sensors 13(10):12,929–12,942 Cowton et al (2018) Cowton J, Kyriazakis I, Plötz T, et al (2018) A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18(8):2521. 10.3390/s18082521, URL https://doi.org/10.3390/s18082521 Doulgerakis et al (2019) Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Cowton J, Kyriazakis I, Plötz T, et al (2018) A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18(8):2521. 10.3390/s18082521, URL https://doi.org/10.3390/s18082521 Doulgerakis et al (2019) Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. 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Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. 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Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. 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In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Chung Y, Oh S, Lee J, et al (2013b) Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems. Sensors 13(10):12,929–12,942 Cowton et al (2018) Cowton J, Kyriazakis I, Plötz T, et al (2018) A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18(8):2521. 10.3390/s18082521, URL https://doi.org/10.3390/s18082521 Doulgerakis et al (2019) Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Cowton J, Kyriazakis I, Plötz T, et al (2018) A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18(8):2521. 10.3390/s18082521, URL https://doi.org/10.3390/s18082521 Doulgerakis et al (2019) Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. 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Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. 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Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. 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Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. 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Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Cowton J, Kyriazakis I, Plötz T, et al (2018) A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18(8):2521. 10.3390/s18082521, URL https://doi.org/10.3390/s18082521 Doulgerakis et al (2019) Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. 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Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Doulgerakis V, Giannousis C, Kalyvas D, et al (2019) An animal welfare platform for extensive livestock production systems. In: AmI Du et al (2020) Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Du X, Carpentier L, Teng G, et al (2020) Assessment of laying hens’ thermal comfort using sound technology. Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. 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URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. 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URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. 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Sensors 20:473. https://doi.org/10.3390/s20020473, URL https://www.mdpi.com/1424-8220/20/2/473 Du et al (2021) Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. 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Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. 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Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. 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Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. 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Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. 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Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. 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Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. 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In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Du X, Teng G, Wang C, et al (2021) A tristimulus-formant model for automatic recognition of call types of laying hens. Computers and Electronics in Agriculture 187:106,221. https://doi.org/10.1016/j.compag.2021.106221, URL https://www.sciencedirect.com/science/article/pii/S0168169921002386 Ferrari et al (2008) Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. 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In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. 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Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Ferrari S, Silva M, Guarino M, et al (2008) Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE 51:1051–1055. 10.13031/2013.24524, URL https://elibrary.asabe.org/abstract.asp?aid=24524 Fonseca et al (2019) Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Fonseca FF, Mamatas L, Viana AC, et al (2019) Personalized travel itineraries with multi-access edge computing touristic services. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. 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In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. 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In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6, 10.1109/GLOBECOM38437.2019.9013548 García et al (2020) García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 García R, Aguilar J, Toro M, et al (2020) A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179:105,826. https://doi.org/10.1016/j.compag.2020.105826, URL https://www.sciencedirect.com/science/article/pii/S0168169920317099 Jadon (2020) Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. 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In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. 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Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. 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URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Jadon S (2020) A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) pp 1–7 Lee et al (2006) Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. 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URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. 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Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Lee CH, Chou CH, Han CC, et al (2006) Automatic recognition of animal vocalizations using averaged mfcc and linear discriminant analysis. Pattern Recognition Letters 27(2):93–101. https://doi.org/10.1016/j.patrec.2005.07.004, URL https://www.sciencedirect.com/science/article/pii/S0167865505001959 Lee et al (2014) Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Lee J, Zuo S, Chung Y, et al (2014) Formant-based acoustic features for cow’s estrus detection in audio surveillance system. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. 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Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. 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Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. 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In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 236–240 Lee et al (2015) Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. Journal of The Royal Society Interface 19(191):20210,921. 10.1098/rsif.2021.0921, URL https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2021.0921, https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0921 Merity (2019) Merity S (2019) Single headed attention RNN: stop thinking with your head. CoRR abs/1911.11423. URL http://arxiv.org/abs/1911.11423, https://arxiv.org/abs/1911.11423 Noda et al (2019) Noda JJ, Travieso-González CM, Sánchez-Rodríguez D, et al (2019) Acoustic classification of singing insects based on mfcc/lfcc fusion. Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Lee J, Noh B, Jang S, et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australasian Journal of Animal Sciences 28:592 – 598 Liz et al (2020) Liz N, Ren Z, Li D, et al (2020) Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal 14:617–625. 10.1017/S1751731119002155, URL https://www.sciencedirect.com/science/article/pii/S1751731119002155 Mahdavian et al (2020) Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture 168:105,100. https://doi.org/10.1016/j.compag.2019.105100, URL https://www.sciencedirect.com/science/article/pii/S0168169918305982 Mao et al (2022) Mao A, Giraudet CSE, Liu K, et al (2022) Automated identification of chicken distress vocalizations using deep learning models. 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Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. 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Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. 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Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. 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Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. 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Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. 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Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. 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Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. 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Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. 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Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. 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Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Mahdavian A, Minaei S, Yang C, et al (2020) Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. 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Applied Sciences 9(19). 10.3390/app9194097 Pattanayak and Pradhan (2021) Pattanayak B, Pradhan G (2021) Pitch-robust acoustic feature using single frequency filtering for children’s kws. Pattern Recognit Lett 150:183–188 Pluk et al (2010) Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. 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- Pluk A, Cangar O, Bahr C, et al (2010) Impact of process related problems on water intake pattern of broiler chicken. Curran Associates Inc., Red Hook, NY, USA, p 29 Sharan and Moir (2017) Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010
- Sharan RV, Moir TJ (2017) Robust acoustic event classification using deep neural networks. Information Sciences 396:24–32. https://doi.org/10.1016/j.ins.2017.02.013, URL https://www.sciencedirect.com/science/article/pii/S0020025517304553 Van Hirtum and Berckmans (2004) Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010
- Van Hirtum A, Berckmans D (2004) Objective recognition of cough sound as biomarker for aerial pollutants. Indoor Air 14(1):10–15. https://doi.org/10.1046/j.1600-0668.2003.00195.x, URL https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1600-0668.2003.00195.x Vandermeulen et al (2016) Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010
- Vandermeulen J, Bahr C, Johnston D, et al (2016) Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds. Computers and Electronics in Agriculture 129:15–26. https://doi.org/10.1016/j.compag.2016.07.014, URL https://www.sciencedirect.com/science/article/pii/S0168169916305105 Vaswani et al (2017) Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010 Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010
- Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Curran Associates Inc., Red Hook, NY, USA, NIPS’17, p 6000–6010
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