Adaptive Diversity-Uncertainty Active Learning with Redundancy Control for Bioacoustic Event Classification
2026-07-06 • Sound
Sound
AI summaryⓘ
The authors developed a smarter way to choose audio samples for teaching a computer to recognize animal sounds, which saves time and effort in labeling data. Their method balances picking uncertain examples with making sure the examples are diverse and not repetitive. They tested this approach on datasets of bird and marine sounds, seeing better learning efficiency and accuracy compared to older methods. This shows that adapting how samples are selected during training can help build better sound recognition models with fewer labeled examples.
active learningbioacousticsmultilabel classificationpredictive uncertaintyembedding spaceMaximum Marginal Relevance (MMR)annotation budgetmean Average Precision (mAP)Area Under the Learning Curve (AULC)
Authors
Gabriel Dubus, Hugo Magaldi, Anatole Gros-Martial
Abstract
Active learning is a promising framework for reducing annotation costs in large-scale bioacoustic monitoring, where expert labeling is expensive and data distributions are highly heterogeneous across environments. However, existing sample selection strategies often rely on static criteria that do not adapt to the evolving reliability of model predictions during training. This limitation can lead to suboptimal exploration-exploitation trade-offs and redundant sample selection. We propose an active learning strategy for multilabel bioacoustic event classification that jointly models predictive uncertainty, embedding-space diversity, and intra-batch redundancy. The method introduces an adaptive weighting scheme that progressively shifts from diversity-driven exploration in high-uncertainty regimes toward uncertainty-driven exploitation as the model becomes more confident, reflecting the increasing reliability of the classifier. To further improve annotation efficiency, a greedy Maximum Marginal Relevance (MMR) procedure is used to enforce diversity among selected samples within each acquisition batch. We evaluate the proposed approach within the BioDCASE 2026 Task 4 active learning framework on terrestrial (BirdSet) and marine (ATBFL) benchmarks using pretrained audio embeddings and a fixed annotation budget. Experimental results show consistent improvements in learning efficiency and competitive in terms of macro mean Average Precision (mAP) and Area Under the Learning Curve (AULC) across heterogeneous acoustic domains. The gains are particularly pronounced on structured terrestrial soundscapes, while performance remains competitive under noisier marine conditions. These findings demonstrate that adaptive acquisition strategies combining uncertainty estimation, embedding-space diversity, and redundancy-aware batch construction provide an effective and robust solution for [...].