Weakly Supervised Detection and Temporal Localization of Whale Calls in Long-Duration Bioacoustic Data
arXiv:2502.20838v3 Announce Type: replace-cross Abstract: Passive acoustic monitoring (PAM) systems generate continuous recordings spanning months, yet automated bioacoustic analysis of whale calls requires two separate annotation efforts: binary presence labels for classification and precise temporal boundaries for localization. A binary label for a multi-minute recording can be assigned in seconds, but timestamping every call within it requires hours of expert effort. Providing both is infeasible at operational scale. We present DSMIL-LocNet, a weakly supervised multiple instance learning (M
The proliferation of passive acoustic monitoring systems generates vast amounts of bioacoustic data, making manual annotation for crucial applications like conservation increasingly infeasible without advanced AI solutions.
This development significantly lowers the barrier for comprehensive bioacoustic data analysis, accelerating scientific discovery and conservation efforts by automating previously labor-intensive tasks.
The ability to accurately detect and localize whale calls with weak supervision transforms the scalability of bioacoustic research, allowing for analysis of much larger datasets than previously possible.
- · Bioacoustics researchers
- · Marine conservation organizations
- · AI/ML developers
- · Environmental monitoring agencies
- · Manual data annotators
More efficient and accurate large-scale analysis of marine wildlife populations becomes possible.
Improved understanding of whale behavior, migration patterns, and the impact of anthropogenic noise on marine ecosystems.
Enhanced policy and regulatory frameworks for marine protection, driven by robust, data-backed insights into ocean health.
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Read at arXiv cs.LG