Motif Mining and Unsupervised Representation Learning for BirdCLEF 2022

Published 8 Jun 2022 in cs.SD, cs.LG, and eess.AS | (2206.04805v1)

Abstract: We build a classification model for the BirdCLEF 2022 challenge using unsupervised methods. We implement an unsupervised representation of the training dataset using a triplet loss on spectrogram representation of audio motifs. Our best model performs with a score of 0.48 on the public leaderboard.

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