mlsanityData to 14 Oct 2026

Motif Mining and Unsupervised Representation Learning for BirdCLEF 2022

arXiv 2206.04805 ↗First posted 8 Jun 2022cs.SD cs.LG eess.AScited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 2 of its 11 references that are in the map. First posted on arXiv 8 Jun 2022.

Builds on

2 of 11 references.

References · 2

  1. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  2. 2018Tile2Vec: Unsupervised representation learning for spatially distributed datacited by 110

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 0; 2026: 1.

3 papers.

  1. 2022Few-shot Long-Tailed Bird Audio Recognitioncited by 1
  2. 2024Automated Bioacoustic Monitoring for South African Bird Species on Unlabeled Datacited by 1
  3. 2026Can Tokens Compete? Token Representations against Supervised CNN Backbones for BirdCLEF+ 2026cited by 0