BioDCASE: Active Learning for Bioacoustics

• URL: http://arxiv.org/abs/2609.15255v1
• Date: Mon, 14 Sep 2026 09:14:50 GMT
• Title: BioDCASE: Active Learning for Bioacoustics
• Abstract summary: We present the 2026 Active Learning for Bioacoustics BioDCASE challenge.<n>A systematic evaluation of sampling methods designed to identify effective AL strategies.<n>Top-ranking submissions combined multiple acquisition signals, and diversity-based selection outperformed pure uncertainty sampling.
• Score: 8.559435146342162
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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