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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.
* Submitted to CEUR-WS under LifeCLEF for the BirdCLEF 2022 challenge
as a working note