Communication Dans Un Congrès Année : 2024

Self-Supervised Learning for Few-Shot Bird Sound Classification

Résumé

Self-supervised learning (SSL) in audio holds significant potential across various domains, particularly in situations where abundant, unlabeled data is readily available at no cost. This is pertinent in bioacoustics, where biologists routinely collect extensive sound datasets from the natural environment. In this study, we demonstrate that SSL is capable of acquiring meaningful representations of bird sounds from audio recordings without the need for annotations. Our experiments showcase that these learned representations exhibit the capacity to generalize to new bird species in few-shot learning (FSL) scenarios. Additionally, we show that selecting windows with high bird activation for self-supervised learning, using a pretrained audio neural network, significantly enhances the quality of the learned representations.

Domaines

Son [cs.SD]
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Dates et versions

hal-04925735 , version 1 (02-02-2025)

Identifiants

  • HAL Id : hal-04925735 , version 1

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Ilyass Moummad, Nicolas Farrugia, Romain Serizel. Self-Supervised Learning for Few-Shot Bird Sound Classification. ICASSPW 2024 SASB, Apr 2024, Seoul, South Korea. ⟨hal-04925735⟩
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