Abstract
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: this https URL
Abstract (translated)
通过被动声学监测（PAM）进行海洋生态系统监测会产生大量数据，但深度学习通常需要精确的注释和较短的数据片段。我们介绍了DSMIL-LocNet框架，这是一个用于仅使用袋级标签检测和定位鲸鱼叫声的多实例学习方法。我们的双流模型处理2至30分钟的音频段，并利用谱图和时序特征以及基于注意力机制的选择实例进行处理。在南极鲸鱼数据上的测试表明，更长的时间上下文可以改善分类性能（F1：0.8-0.9），而中等长度的数据片段则确保了定位精度（0.65-0.70）。这表明多实例学习可以增强海洋监测的可扩展性。代码位于此链接：[此处插入实际链接]
URL
https://arxiv.org/abs/2502.20838
