Acoustic scene classification using convolutional neural network and multiple-width frequency-delta data augmentation

Abstract: In recent years, neural network approaches have shown superior performance to conventional hand-made features in numerous application areas. In particular, convolutional neural networks (ConvNets) exploit spatially local correlations across input data to improve the performance of audio processing tasks, such as speech recognition, musical chord recognition, and onset detection. Here we apply ConvNet to acoustic scene classification, and show that the error rate can be further decreased by using delta features in the frequency domain. We propose a multiple-width frequency-delta (MWFD) data augmentation method that uses static mel-spectrogram and frequency-delta features as individual input examples. In addition, we describe a ConvNet output aggregation method designed for MWFD augmentation, folded mean aggregation, which combines output probabilities of static and MWFD features from the same analysis window using multiplication first, rather than taking an average of all output probabilities. We describe calculation results using the DCASE 2016 challenge dataset, which shows that ConvNet outperforms both of the baseline system with hand-crafted features and a deep neural network approach by around 7%. The performance was further improved (by 5.7%) using the MWFD augmentation together with folded mean aggregation. The system exhibited a classification accuracy of 0.831 when classifying 15 acoustic scenes.