仅用记录级标签实现鲸鸣的自动检测与定位，省去繁琐标注。
Weakly Supervised Detection and Temporal Localization of Whale Calls in Long-Duration Bioacoustic Data
- 基于弱监督的多实例学习框架，融合频谱与时间特征处理长音频。
- 在300-1800秒录音上F1达0.88-0.91，远超需帧级标注的基线模型。
- 适合生物声学监测、长期生态研究等无需精细标注的应用场景。
被动声学监测（PAM）系统产生持续数月的录音，但自动化分析鲸类叫声需两类标注：分类用的有无标签和定位用的精确时间边界。一个数分钟录音的有无标签可在数秒内完成，但标注其中每个叫声则需数小时专家工作。同时提供两类标注在实际操作中不可行。本文提出DSMIL-LocNet，一种仅使用记录级存在/缺失标签的弱监督多实例学习（MIL）框架，可同时完成分类与时间定位。其双流架构结合频谱与时间特征，处理2–30分钟音频，避免现有CNN方法在长输入时的时间压缩问题。在AcousticTrends BlueFinLibrary数据集上，对300–1800秒录音，该模型取得0.88–0.91的F1分数，而全监督CNN基线模型性能下降至0.19–0.64。此外，该方法能提供基线无法实现的时间定位结果。代码已开源。
原文摘要 · Abstract (English)
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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