基于语义分割的大规模生物声学检测：应用于海底地震仪记录中长须鲸叫声的深度学习框架
Large-scale bioacoustic detection using semantic segmentation: a deep learning framework applied to fin whale calls in ocean-bottom seismometer recordings
- University College London（伦敦大学学院）
- Universidade dos Açores（亚速尔大学）
- Institute of Marine Sciences, CSIC（西班牙国家研究委员会海洋科学研究所）
- University of St Andrews（圣安德鲁斯大学）
- Instituto do Mar (IMAR)（海洋研究所（IMAR））
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出深度学习语义分割框架，在OBS记录中检测长须鲸20赫兹叫声，跨传感器泛化，识别630万叫声，构建最大目录，扩展被动声学监测范围。
AI中文摘要：
海底地震仪（OBS）最初为地球物理研究而部署，在广阔海域连续记录低频声音数月乃至数年，为须鲸的被动声学监测（PAM）提供了大量尚未开发的资源。实现这一潜力需要能够在大型传感器网络的各种条件下可靠运行的自动化检测方法。我们提出了一种深度学习语义分割框架，用于在OBS频谱图中检测长须鲸（Balaenoptera physalus）的20赫兹音符，为每个像素分配属于叫声的概率，并将所得概率图转换为描述单个检测结果的时频边界框。我们在亚速尔-马德拉-加那利群岛地区一次OBS部署的水听器数据上训练了该模型，并在未重新训练的情况下将其应用于来自第二个地理上不同部署的垂直分量地震仪数据，表明单一训练模型可跨传感器类型和记录环境进行泛化。将该检测器应用于来自46个OBS站点的378,912小时记录，识别出630万个叫声，构成了迄今为止最大的长须鲸叫声目录，在两个部署中均具有高精度（约97%）。所得目录能够足够准确地解析叫声时序和频谱结构，以支持生态学分析，揭示了歌唱季节中三个持续存在的音符间隔（INI）组的一致季节性变化以及盆地尺度的叫声活动模式。通过将现有地球物理基础设施转变为可扩展的传感网络，我们的方法在不进行新硬件投资的情况下大幅扩展了PAM的空间和时间覆盖范围，为追踪其他低频发声物种提供了一个可迁移的框架，并为大规模保护规划、海洋空间管理和丰度估算提供信息。
英文摘要：
Ocean-bottom seismometers (OBS), originally deployed for geophysical research, continuously record low-frequency sound for months to years across broad areas of ocean, offering a largely untapped resource for passive acoustic monitoring (PAM) of baleen whales. Realising this potential requires automated detection methods that operate reliably across the varied conditions in large sensor networks. We present a deep learning semantic segmentation framework that detects the 20-Hz notes of fin whales (Balaenoptera physalus) in OBS spectrograms, assigning each pixel a probability of belonging to a call and converting the resulting probability maps into time-frequency bounding boxes describing individual detections. We trained the model on hydrophone data from one OBS deployment in the Azores-Madeira-Canaries region and applied it without retraining to vertical-component seismometer data from a second, geographically distinct deployment, showing that a single trained model generalises across sensor types and recording environments. Applied to 378,912 h of recordings from 46 OBS sites, the detector identified 6.3 million calls, forming the largest fin whale call catalogue assembled to date, with high precision (~97%) across both deployments. The resulting catalogue resolves call timing and spectral structure accurately enough to support ecological analyses, revealing coherent seasonal shifts in three persistent inter-note interval (INI) groups across the singing season and basin-scale patterns in calling activity. By transforming existing geophysical infrastructure into a scalable sensing network, our approach substantially expands the spatial and temporal reach of PAM without new hardware investment, offering a transferable framework for tracking other low-frequency vocalising species and informing conservation planning, marine spatial management, and abundance estimation across large scales.
