mlsanityData to 7 Oct 2026

Towards Deep Active Learning in Avian Bioacoustics

arXiv 2406.18621 ↗First posted 26 Jun 2024cs.SD cs.AI eess.AScited by 10

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Abstract (arXiv)

Passive acoustic monitoring (PAM) in avian bioacoustics enables cost-effective and extensive data collection with minimal disruption to natural habitats. Despite advancements in computational avian bioacoustics, deep learning models continue to encounter challenges in adapting to diverse environments in practical PAM scenarios. This is primarily due to the scarcity of annotations, which requires labor-intensive efforts from human experts. Active learning (AL) reduces annotation cost and speed ups adaption to diverse scenarios by querying the most informative instances for labeling. This paper outlines a deep AL approach, introduces key challenges, and conducts a small-scale pilot study.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 12 of its 23 references that are in the map. First posted on arXiv 26 Jun 2024.

Builds on

12 of 23 references.

References · 12

  1. 2019On the Variance of the Adaptive Learning Rate and Beyondcited by 763
  2. 2019Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Boundscited by 602
  3. 2022Active Learning on a Budget: Opposite Strategies Suit High and Low Budgetscited by 131
  4. 2021Computational bioacoustics with deep learning: a review and roadmapcited by 78
  5. 2023Global birdsong embeddings enable superior transfer learning for bioacoustic classificationcited by 51
  6. 2024BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacousticscited by 36
  7. 2021Improving Bird Classification with Unsupervised Sound Separationcited by 25
  8. 2023BIRB: A Generalization Benchmark for Information Retrieval in Bioacousticscited by 19
  9. 2023ActiveGLAE: A Benchmark for Deep Active Learning with Transformerscited by 14
  10. 2024Fast Fishing: Approximating BAIT for Efficient and Scalable Deep Active Image Classificationcited by 6
  11. 2023DADO -- Low-Cost Query Strategies for Deep Active Design Optimizationcited by 4
  12. 2023Active Bird2Vec: Towards End-to-End Bird Sound Monitoring with Transformerscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 6; 2026: 4.

10 papers.

  1. 2025Can Masked Autoencoders Also Listen to Birds?cited by 26
  2. 2025Foundation Models for Bioacoustics -- a Comparative Reviewcited by 11
  3. 2025Unmute the Patch Tokens: Rethinking Probing in Multi-Label Audio Classificationcited by 7
  4. 2025Hybrid Disagreement-Diversity Active Learning for Bioacoustic Sound Event Detectioncited by 2
  5. 2025No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Successcited by 1
  6. 2025Data-Efficient Self-Supervised Algorithms for Fine-Grained Birdsong Analysiscited by 1
  7. 2026BoSS: A Best-of-Strategies Selector as an Oracle for Deep Active Learningcited by 1
  8. 2026BioDCASE: Active Learning for Bioacousticscited by 1
  9. 2026BAT: Better Audio Transformer Guided by Convex Gated Probingcited by 0
  10. 2026Active Learning for Biodiversity Monitoring: From Label Efficiency to Reliable Ecological Inferencecited by 0