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arXiv · 2502.20838

Weakly Supervised Detection and Temporal Localization of Whale Calls in Long-Duration Bioacoustic Data

Ragib Amin Nihal·Benjamin Yen·Runwu Shi·Takeshi Ashizawa·Kazuhiro Nakadai
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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 (MIL) framework that performs both classification and temporal localization using only recording-level presence/absence labels. Our dual-stream architecture integrates spectral and temporal features to process recordings of 2--30 minutes without the temporal compression that degrades existing CNN methods on long inputs. On the AcousticTrends BlueFinLibrary, DSMIL-LocNet achieves F1 scores of 0.88--0.91 on recordings of 300--1800s, where fully supervised CNN baselines degrade to 0.19--0.64. It also provides temporal localization that these baselines cannot produce without frame-level annotation. Code: https://github.com/Ragib-Amin-Nihal/DSMIL-Loc

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Ragib Amin Nihal, Benjamin Yen, Runwu Shi, Takeshi Ashizawa, Kazuhiro Nakadai. 2026-05-28. Weakly Supervised Detection and Temporal Localization of Whale Calls in Long-Duration Bioacoustic Data. https://arxiv.org/abs/2502.20838

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