发表机构
University of Amsterdam; Massachusetts Institute of Technology; Tampere University; Earth Species Project; Kassel University（阿姆斯特丹大学; 麻省理工学院; 坦佩雷大学; 地球物种项目; 卡塞尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对被动声学监测中标注成本高的问题，本文提出BioDCASE挑战赛，系统评估多种主动学习采样方法，发现结合多样性与不确定性信号并减少批次冗余可显著提升性能，最高比随机采样高67.1%。
AI 中文摘要
生态监测日益依赖机器学习模型，其性能取决于标注数据的质量和数量。然而，获取这些标注成本高昂，尤其是在被动声学监测中，虽然收集了大量数据，但实际可标注的比例很小。主动学习通过优先选择应标注的样本来解决这一瓶颈。然而，由于已发表的方法在不同模型、预算、评估指标和数据集下进行评估，进展难以衡量。为应对这一挑战，我们提出了2026年生物声学主动学习BioDCASE挑战赛：一项系统评估采样方法的研究，旨在识别有效的主动学习策略。参与者的方法在由陆地和海洋数据组成的四个子集上进行了评估。在来自七个团队的十种提议采样方法中，排名最高的方法在相同标注预算下，其学习曲线下面积比随机采样平均高出26.4%（四个数据子集的平均值）。各子集间的性能差异显著，表现最佳的提交在HSN子集上比随机采样高出67.1%，在ATBFL子集上高出8%。排名靠前的提交结合了多种采集信号，且基于多样性的选择优于纯不确定性采样。此外，有证据表明，从基于多样性的选择转向基于不确定性的选择，并明确减少采集批次内的冗余，可改善模型训练。初步证据还表明，在标注过程后期，较大的采集批次大小可能越来越有益。
英文摘要
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.
CommentsThis paper summarises the BioDCASE Active Learning for Bioacoustics data challenge. This paper was reviewed and accepted to the non-archival track of the ECCV Computer Vision for Ecology workshop via OpenReview
