mlsanityData to 14 Oct 2026

Self-Supervised Learning for Few-Shot Bird Sound Classification

arXiv 2312.15824 ↗First posted 25 Dec 2023cs.SD cs.LG eess.AScited by 11

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Abstract (arXiv)

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.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 20 of its 24 references that are in the map. First posted on arXiv 25 Dec 2023.

Builds on

20 of 24 references.

References · 20

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  2. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,974
  3. 2020Supervised Contrastive Learningcited by 2,829
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  11. 2019SpecAugment on Large Scale Datasetscited by 60
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8 more
  1. 2017Unsupervised Learning of Semantic Audio Representationscited by 44
  2. 2022BYOL for Audio: Exploring Pre-trained General-purpose Audio Representationscited by 36
  3. 2020Unsupervised Contrastive Learning of Sound Event Representationscited by 32
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  6. 2022MetaAudio: A Few-Shot Audio Classification Benchmarkcited by 24
  7. 2023Learning to detect an animal sound from five examplescited by 14
  8. 2023Regularized Contrastive Pre-training for Few-shot Bioacoustic Sound Detectioncited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 4; 2026: 2.

11 papers.

  1. 2024BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacousticscited by 36
  2. 2025Can Masked Autoencoders Also Listen to Birds?cited by 26
  3. 2025Comparing Self-Supervised Learning Models Pre-Trained on Human Speech and Animal Vocalizations for Bioacoustics Processingcited by 9
  4. 2024Leveraging tropical reef, bird and unrelated sounds for superior transfer learning in marine bioacousticscited by 7
  5. 2024Mixture of Mixups for Multi-label Classification of Rare Anuran Soundscited by 4
  6. 2024Acoustic identification of individual animals with hierarchical contrastive learningcited by 2
  7. 2025An Automated Pipeline for Few-Shot Bird Call Classification: A Case Study with the Tooth-Billed Pigeoncited by 2
  8. 2024Generative AI-based data augmentation for improved bioacoustic classification in noisy environmentscited by 1
  9. 2025Localized time-frequency representation learning for bioacoustic classification in complex soundscapescited by 1
  10. 2026CoarseSoundNet: Building a reliable model for ecological soundscape analysiscited by 0
  11. 2026MAST: Label-Efficient, Robust, and Generalizable Sound Detection for Biodiversity Monitoring via Masked Audio Pretraining and Self-Trainingcited by 0