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Motif Mining and Unsupervised Representation Learning for BirdCLEF 2022

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arxiv 2206.04805 v1 pith:LWMNMUTT submitted 2022-06-08 cs.SD cs.LGeess.AS

Anthony Miyaguchi , Jiangyue Yu , Bryan Cheungvivatpant , Dakota Dudley , Aniketh Swain This is my paper · ORCID
classification cs.SDcs.LGeess.AS
keywords representationunsupervisedbirdclefmodelaudiobestbuildchallenge
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 1 Pith paper

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  1. Can Tokens Compete? Token Representations against Supervised CNN Backbones for BirdCLEF+ 2026

    cs.SD 2026-07 conditional novelty 5.0 of 10

    For BirdCLEF+ 2026, a frozen Perch-v2 probe plus a trained HGNetV2-B0 SED net and non-bird prototype heads reach private LB 0.936, while WavTokenizer codec tokens collapse and four general audio transformers lag under...

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