mlsanityData to 7 Oct 2026

Regularized Contrastive Pre-training for Few-shot Bioacoustic Sound Detection

arXiv 2309.08971 ↗First posted 16 Sep 2023cs.SD cs.LG eess.AScited by 7

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Abstract (arXiv)

Bioacoustic sound event detection allows for better understanding of animal behavior and for better monitoring biodiversity using audio. Deep learning systems can help achieve this goal, however it is difficult to acquire sufficient annotated data to train these systems from scratch. To address this limitation, the Detection and Classification of Acoustic Scenes and Events (DCASE) community has recasted the problem within the framework of few-shot learning and organize an annual challenge for learning to detect animal sounds from only five annotated examples. In this work, we regularize supervised contrastive pre-training to learn features that can transfer well on new target tasks with animal sounds unseen during training, achieving a high F-score of 61.52%(0.48) when no feature adaptation is applied, and an F-score of 68.19%(0.75) when we further adapt the learned features for each new target task. This work aims to lower the entry bar to few-shot bioacoustic sound event detection by proposing a simple and yet effective framework for this task, by also providing open-source code.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 15 of its 20 references that are in the map. First posted on arXiv 16 Sep 2023.

Builds on

15 of 20 references.

References · 15

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  2. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  3. 2020Supervised Contrastive Learningcited by 2,803
  4. 2021Barlow Twins: Self-Supervised Learning via Redundancy Reductioncited by 1,818
  5. 2021VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learningcited by 1,013
  6. 2021Understanding Dimensional Collapse in Contrastive Self-supervised Learningcited by 374
  7. 2020Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reductioncited by 141
  8. 2021Decoupled Contrastive Learningcited by 141
  9. 2021Computational bioacoustics with deep learning: a review and roadmapcited by 78
  10. 2020Unsupervised Contrastive Learning of Sound Event Representationscited by 32
  11. 2022MetaAudio: A Few-Shot Audio Classification Benchmarkcited by 24
  12. 2023EMP-SSL: Towards Self-Supervised Learning in One Training Epochcited by 18
3 more
  1. 2023Learning to detect an animal sound from five examplescited by 14
  2. 2022Surrey System for DCASE 2022 Task 5: Few-shot Bioacoustic Event Detection with Segment-level Metric Learningcited by 3
  3. 2023Pretraining Representations for Bioacoustic Few-shot Detection using Supervised Contrastive Learningcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 3; 2026: 1.

7 papers.

  1. 2023Self-Supervised Learning for Few-Shot Bird Sound Classificationcited by 11
  2. 2024Animal Behavior Analysis Methods Using Deep Learning: A Surveycited by 4
  3. 2024Acoustic identification of individual animals with hierarchical contrastive learningcited by 2
  4. 2025Synthetic data enables context-aware bioacoustic sound event detectioncited by 2
  5. 2025Hybrid Disagreement-Diversity Active Learning for Bioacoustic Sound Event Detectioncited by 2
  6. 2026bacpipe: a Python package to make bioacoustic deep learning models accessiblecited by 1
  7. 2025A Comprehensive Survey of Challenges and Opportunities of Few-Shot Learning Across Multiple Domainscited by 0