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Motif Mining and Unsupervised Representation Learning for BirdCLEF 2022

2022-06-08Code Available0· sign in to hype

Anthony Miyaguchi, Jiangyue Yu, Bryan Cheungvivatpant, Dakota Dudley, Aniketh Swain

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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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