2 papers
cs.SD2026
A Self-Supervised Approach for Minimal-Annotation Hydroacoustic Data Exploration
Pierre-Yves Raumer, Axel Marmoret, Dorian Cazau +6
Passive hydroacoustic monitoring often generates large volumes of continuous recordings that are only partially exploited due to the cost of manual annotation. Supervised detection…
cs.SD2026
Adaptive Diversity-Uncertainty Active Learning with Redundancy Control for Bioacoustic Event Classification
Gabriel Dubus, Hugo Magaldi, Anatole Gros-Martial
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 h…