Zhong Ming, Castellote Manuel, Dodhia Rahul, Lavista Ferres Juan, Keogh Mandy, Brewer Arial
AI for Good Research Lab, Microsoft, Redmond, Washington 98052, USA.
Alaska Fisheries Science Center-National Oceanic and Atmospheric Administration (NOAA) Fisheries and Joint Institute for the Study of the Atmosphere and Ocean (JISAO), University of Washington, Seattle, Washington 98195, USA.
J Acoust Soc Am. 2020 Mar;147(3):1834. doi: 10.1121/10.0000921.
Over a decade after the Cook Inlet beluga (Delphinapterus leucas) was listed as endangered in 2008, the population has shown no sign of recovery. Lack of ecological knowledge limits the understanding of, and ability to manage, potential threats impeding recovery of this declining population. National Oceanic and Atmospheric Administration Fisheries, in partnership with the Alaska Department of Fish and Game, initiated a passive acoustics monitoring program in 2017 to investigate beluga seasonal occurrence by deploying a series of passive acoustic moorings. Data have been processed with semi-automated tonal detectors followed by time intensive manual validation. To reduce this labor intensive and time-consuming process, in addition to increasing the accuracy of classification results, the authors constructed an ensembled deep learning convolutional neural network model to classify beluga detections as true or false. Using a 0.5 threshold, the final model achieves 96.57% precision and 92.26% recall on testing dataset. This methodology proves to be successful at classifying beluga signals, and the framework can be easily generalized to other acoustic classification problems.
2008年库克湾白鲸（白鲸属）被列为濒危物种十多年后，其种群数量仍没有恢复的迹象。生态知识的匮乏限制了人们对阻碍这一数量不断下降的种群恢复的潜在威胁的理解以及应对能力。美国国家海洋和大气管理局渔业部门与阿拉斯加鱼类和野生动物部合作，于2017年启动了一项被动声学监测计划，通过部署一系列被动声学系留装置来调查白鲸的季节性出现情况。数据已通过半自动音调探测器进行处理，随后进行耗时的人工验证。为了减少这一劳动密集且耗时的过程，同时提高分类结果的准确性，作者构建了一个集成深度学习卷积神经网络模型，用于将白鲸探测结果分类为真或假。使用0.5的阈值，最终模型在测试数据集上的精确率达到96.57%，召回率达到92.26%。该方法在对白鲸信号进行分类方面被证明是成功的，并且该框架可以很容易地推广到其他声学分类问题。
