Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, United States of America.
Northeast Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Woods Hole, Massachusetts, United States of America.
PLoS One. 2024 Jun 4;19(6):e0304744. doi: 10.1371/journal.pone.0304744. eCollection 2024.
Passive acoustic monitoring is an essential tool for studying beaked whale populations. This approach can monitor elusive and pelagic species, but the volume of data it generates has overwhelmed researchers' ability to quantify species occurrence for effective conservation and management efforts. Automation of data processing is crucial, and machine learning algorithms can rapidly identify species using their sounds. Beaked whale acoustic events, often infrequent and ephemeral, can be missed when co-occurring with signals of more abundant, and acoustically active species that dominate acoustic recordings. Prior efforts on large-scale classification of beaked whale signals with deep neural networks (DNNs) have approached the class as one of many classes, including other odontocete species and anthropogenic signals. That approach tends to miss ephemeral events in favor of more common and dominant classes. Here, we describe a DNN method for improved classification of beaked whale species using an extensive dataset from the western North Atlantic. We demonstrate that by training a DNN to focus on the taxonomic family of beaked whales, ephemeral events were correctly and efficiently identified to species, even with few echolocation clicks. By retrieving ephemeral events, this method can support improved estimation of beaked whale occurrence in regions of high odontocete acoustic activity.
被动声学监测是研究喙鲸种群的重要工具。这种方法可以监测难以捉摸的远洋物种，但它生成的数据量已经超过了研究人员对有效保护和管理工作进行物种出现量化的能力。数据处理的自动化至关重要，机器学习算法可以利用它们的声音快速识别物种。当与更丰富和声音活跃的物种的信号同时出现时，喙鲸的声音事件往往很少见且短暂，可能会被忽略，而这些物种在声学记录中占主导地位。以前使用深度神经网络（DNN）对喙鲸信号进行大规模分类的努力将该类作为许多类之一，包括其他齿鲸物种和人为信号。这种方法往往会忽略短暂的事件，而倾向于更常见和占主导地位的类别。在这里，我们描述了一种使用北大西洋西部的大量数据集来改进喙鲸物种分类的 DNN 方法。我们证明，通过训练 DNN 专注于喙鲸科，可以正确有效地将短暂的事件识别到物种，即使只有很少的回声定位点击。通过检索短暂的事件，这种方法可以支持在齿鲸声音活动较高的区域更好地估计喙鲸的出现。
