# BioDCASE: Active Learning for Bioacoustics

arXiv: https://arxiv.org/abs/2609.15255 · HTML: https://mlsanity.com/paper/2609.15255/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 38 references that are in the map.
- First posted on arXiv 14 Sep 2026.

## Builds on (most cited first, 19 of 19)

- [Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets](https://mlsanity.com/paper/2202.02794/) (2022)
- [Batch Active Learning at Scale](https://mlsanity.com/paper/2107.14263/) (2021)
- [On Statistical Bias In Active Learning: How and When To Fix It](https://mlsanity.com/paper/2101.11665/) (2021)
- [Practical Obstacles to Deploying Active Learning](https://mlsanity.com/paper/1807.04801/) (2018)
- [Towards Robust and Reproducible Active Learning Using Neural Networks](https://mlsanity.com/paper/2002.09564/) (2020)
- [BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics](https://mlsanity.com/paper/2403.10380/) (2024)
- [Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance Assessment](https://mlsanity.com/paper/2301.10625/) (2023)
- [Can Masked Autoencoders Also Listen to Birds?](https://mlsanity.com/paper/2504.12880/) (2025)
- [Perch 2.0: The Bittern Lesson for Bioacoustics](https://mlsanity.com/paper/2508.04665/) (2025)
- [Foundation Models for Bioacoustics -- a Comparative Review](https://mlsanity.com/paper/2508.01277/) (2025)
- [Towards Deep Active Learning in Avian Bioacoustics](https://mlsanity.com/paper/2406.18621/) (2024)
- [A decade of DCASE: Achievements, practices, evaluations and future challenges](https://mlsanity.com/paper/2410.04951/) (2024)
- [The Search for Squawk: Agile Modeling in Bioacoustics](https://mlsanity.com/paper/2505.03071/) (2025)
- [Perch 2.0 transfers 'whale' to underwater tasks](https://mlsanity.com/paper/2512.03219/) (2025)
- [Finding Needles in the Haystack: Transductive Active Labeling in Ecology](https://mlsanity.com/paper/2606.03821/) (2026)
- [BoSS: A Best-of-Strategies Selector as an Oracle for Deep Active Learning](https://mlsanity.com/paper/2603.13109/) (2026)
- [bacpipe: a Python package to make bioacoustic deep learning models accessible](https://mlsanity.com/paper/2604.11560/) (2026)
- [Adaptive Diversity-Uncertainty Active Learning with Redundancy Control for Bioacoustic Event Classification](https://mlsanity.com/paper/2607.04868/) (2026)
- [Determinantal point process sampling for bioacoustic active learning](https://mlsanity.com/paper/2607.06063/) (2026)

## Built on by (most cited first, 1 of 1)

- [Active Learning for Biodiversity Monitoring: From Label Efficiency to Reliable Ecological Inference](https://mlsanity.com/paper/2609.27409/) (2026)

## Abstract (arXiv)

Ecological monitoring increasingly relies on machine learning models, whose performance depends on the quality and quantity of labelled data. However, obtaining these labels is costly, particularly in passive acoustic monitoring, where vast amounts of data are collected but only a small proportion can feasibly be annotated. Active learning addresses this bottleneck by prioritizing which samples should be labelled. However, progress is difficult to measure, because published methods are evaluated under different models, budgets, evaluation metrics and datasets. To address this challenge, we present the 2026 Active Learning for Bioacoustics BioDCASE challenge: a systematic evaluation of sampling methods designed to identify effective AL strategies. Participant methods were evaluated across four subsets composed of terrestrial and marine data. Across ten proposed sampling methods from seven teams, the top-ranked method achieved an area under the learning curve 26.4 % higher than random sampling at the same annotation budget, averaged over four data subsets. Significant variation in performance was observed across subsets, with the top-performing submission achieving a 67.1 % gain for the HSN subset over random sampling and a gain of 8 % for the ATBFL subset. Top-ranking submissions combined multiple acquisition signals, and diversity-based selection outperformed pure uncertainty sampling. Furthermore, there is evidence that transitioning from diversity-based to uncertainty-based selection and explicitly reducing redundancy within acquisition batches improve model training. There is also initial evidence that larger acquisition batch sizes may be increasingly beneficial later in the labelling process.