Biodiversity Research Center, Academia Sinica, Taipei, Taiwan (R.O.C).
The Ocean Policy Research Institute, The Sasakawa Peace Foundation, Tokyo, Japan.
PLoS Comput Biol. 2021 Feb 18;17(2):e1008698. doi: 10.1371/journal.pcbi.1008698. eCollection 2021 Feb.
Remote acquisition of information on ecosystem dynamics is essential for conservation management, especially for the deep ocean. Soundscape offers unique opportunities to study the behavior of soniferous marine animals and their interactions with various noise-generating activities at a fine temporal resolution. However, the retrieval of soundscape information remains challenging owing to limitations in audio analysis techniques that are effective in the face of highly variable interfering sources. This study investigated the application of a seafloor acoustic observatory as a long-term platform for observing marine ecosystem dynamics through audio source separation. A source separation model based on the assumption of source-specific periodicity was used to factorize time-frequency representations of long-duration underwater recordings. With minimal supervision, the model learned to discriminate source-specific spectral features and prove to be effective in the separation of sounds made by cetaceans, soniferous fish, and abiotic sources from the deep-water soundscapes off northeastern Taiwan. Results revealed phenological differences among the sound sources and identified diurnal and seasonal interactions between cetaceans and soniferous fish. The application of clustering to source separation results generated a database featuring the diversity of soundscapes and revealed a compositional shift in clusters of cetacean vocalizations and fish choruses during diurnal and seasonal cycles. The source separation model enables the transformation of single-channel audio into multiple channels encoding the dynamics of biophony, geophony, and anthropophony, which are essential for characterizing the community of soniferous animals, quality of acoustic habitat, and their interactions. Our results demonstrated the application of source separation could facilitate acoustic diversity assessment, which is a crucial task in soundscape-based ecosystem monitoring. Future implementation of soundscape information retrieval in long-term marine observation networks will lead to the use of soundscapes as a new tool for conservation management in an increasingly noisy ocean.
远程获取生态系统动态信息对于保护管理至关重要，特别是对于深海而言。声景为研究发声海洋动物的行为及其与各种噪声源活动的相互作用提供了独特的机会，具有精细的时间分辨率。然而，由于音频分析技术在面对高度变化的干扰源时效果有限，因此检索声景信息仍然具有挑战性。本研究探讨了海底声学观测站作为通过音频源分离长期观察海洋生态系统动态的平台的应用。基于源特定周期性假设的源分离模型用于对长时间水下记录的时频表示进行因子分解。该模型在最小监督的情况下，学会了区分特定源的光谱特征，并在分离台湾东北部深海声景中的鲸目动物、发声鱼类和无生命声源的声音方面表现出有效性。结果揭示了声源之间的物候差异，并确定了鲸目动物和发声鱼类之间的昼夜和季节相互作用。对源分离结果进行聚类的应用生成了一个具有声景多样性的数据库，并揭示了在昼夜和季节周期中鲸目动物发声和鱼类合唱集群的组成变化。源分离模型能够将单声道音频转换为多个通道，编码生物声、地声和人为声的动态，这对于描述发声动物群落、声学栖息地质量及其相互作用至关重要。我们的结果表明，源分离的应用可以促进声学多样性评估，这是基于声景的生态系统监测中的一项关键任务。未来在长期海洋观测网络中实施声景信息检索将导致声景作为日益嘈杂海洋中的保护管理新工具的使用。
