Weakly Supervised Multiple Instance Learning for Whale Call Detection and Temporal Localization in Long-Duration Passive Acoustic Monitoring
This addresses scalable marine monitoring for researchers by reducing annotation needs, though it is incremental as it builds on existing MIL methods.
The paper tackles whale call detection and localization in long-duration audio using only bag-level labels, achieving F1 scores of 0.8-0.9 for classification and localization precision of 0.65-0.70.
Marine ecosystem monitoring via Passive Acoustic Monitoring (PAM) generates vast data, but deep learning often requires precise annotations and short segments. We introduce DSMIL-LocNet, a Multiple Instance Learning framework for whale call detection and localization using only bag-level labels. Our dual-stream model processes 2-30 minute audio segments, leveraging spectral and temporal features with attention-based instance selection. Tests on Antarctic whale data show longer contexts improve classification (F1: 0.8-0.9) while medium instances ensure localization precision (0.65-0.70). This suggests MIL can enhance scalable marine monitoring. Code: https://github.com/Ragib-Amin-Nihal/DSMIL-Loc