# Towards Deep Active Learning in Avian Bioacoustics

arXiv: https://arxiv.org/abs/2406.18621 · HTML: https://mlsanity.com/paper/2406.18621/

## 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 (most cited first, 12 of 12)

- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets](https://mlsanity.com/paper/2202.02794/) (2022)
- [Computational bioacoustics with deep learning: a review and roadmap](https://mlsanity.com/paper/2112.06725/) (2021)
- [Global birdsong embeddings enable superior transfer learning for bioacoustic classification](https://mlsanity.com/paper/2307.06292/) (2023)
- [BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics](https://mlsanity.com/paper/2403.10380/) (2024)
- [Improving Bird Classification with Unsupervised Sound Separation](https://mlsanity.com/paper/2110.03209/) (2021)
- [BIRB: A Generalization Benchmark for Information Retrieval in Bioacoustics](https://mlsanity.com/paper/2312.07439/) (2023)
- [ActiveGLAE: A Benchmark for Deep Active Learning with Transformers](https://mlsanity.com/paper/2306.10087/) (2023)
- [Fast Fishing: Approximating BAIT for Efficient and Scalable Deep Active Image Classification](https://mlsanity.com/paper/2404.08981/) (2024)
- [DADO -- Low-Cost Query Strategies for Deep Active Design Optimization](https://mlsanity.com/paper/2307.04536/) (2023)
- [Active Bird2Vec: Towards End-to-End Bird Sound Monitoring with Transformers](https://mlsanity.com/paper/2308.07121/) (2023)

## Built on by (most cited first, 10 of 10)

- [Can Masked Autoencoders Also Listen to Birds?](https://mlsanity.com/paper/2504.12880/) (2025)
- [Foundation Models for Bioacoustics -- a Comparative Review](https://mlsanity.com/paper/2508.01277/) (2025)
- [Unmute the Patch Tokens: Rethinking Probing in Multi-Label Audio Classification](https://mlsanity.com/paper/2509.24901/) (2025)
- [Hybrid Disagreement-Diversity Active Learning for Bioacoustic Sound Event Detection](https://mlsanity.com/paper/2505.20956/) (2025)
- [No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success](https://mlsanity.com/paper/2506.01992/) (2025)
- [Data-Efficient Self-Supervised Algorithms for Fine-Grained Birdsong Analysis](https://mlsanity.com/paper/2511.12158/) (2025)
- [BoSS: A Best-of-Strategies Selector as an Oracle for Deep Active Learning](https://mlsanity.com/paper/2603.13109/) (2026)
- [BioDCASE: Active Learning for Bioacoustics](https://mlsanity.com/paper/2609.15255/) (2026)
- [BAT: Better Audio Transformer Guided by Convex Gated Probing](https://mlsanity.com/paper/2602.16305/) (2026)
- [Active Learning for Biodiversity Monitoring: From Label Efficiency to Reliable Ecological Inference](https://mlsanity.com/paper/2609.27409/) (2026)

## 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.