奥里亚岛声学的深度学习:在奥里亚岛实验室进行为期三年的五声道连续录音,揭示潮汐、月亮和日蚀效应

Marion Poupard, Paul Best, Jan Schlüter, Jean-Marc Prevot, H. Symonds, P. Spong, H. Glotin
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引用次数: 6

摘要

研究在水下环境中产生信号的动物的最好方法之一是使用被动声监测(PAM)。声学监测用于研究海洋中的海洋哺乳动物,并为我们提供了解鲸类动物生活的信息,例如它们的行为、运动或繁殖。由于数据量大,对捕获的声音进行自动分析几乎是必不可少的。这项任务选择了深度学习方法,因为它已被证明在回答此类问题方面效率很高。这项研究的重点是加拿大北温哥华岛的逆戟鲸(Orcinus orca),与非政府组织Orcalab合作,在汉森岛周围开发了一个多水听器录音站来研究逆戟鲸。该水声站由5个水听器组成,覆盖面积超过50平方公里。自2016年以来,我们不断将水听器信号流式传输到法国土伦的实验室,产生近50 TB的同步多通道录音。这项研究的目的是对收集到的数据进行初步分析,并证明环境因素(潮汐、月相和日周期)对逆戟鲸声学活动的影响。
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Deep Learning for Ethoacoustics of Oreas on three years pentaphonie continuous recording at Orealab revealing tide, moon and diel effects
One of the best ways of studying animals that produce signals in underwater environments is to use passive acoustic monitoring (PAM). Acoustic monitoring is used to study marine mammals in oceans, and gives us information for understanding cetacean life, such as their behaviour, movement or reproduction. Automated analysis for captured sound is almost essential because of the large quantity of data. A deep learning approach was chosen for this task, since it has proven great efficiency for answering such problematics. This study focused on the orcas (Orcinus orca) of northern Vancouver Island, Canada, in collaboration with the NGO Orcalab which developed a multi-hydrophone recording station around Hanson Island to study orcas. The acoustic station is composed of 5 hydrophones and extends over 50 km2 of ocean. Since 2016 we are continuously streaming the hydrophone signals to our laboratory at Toulon, France, yielding nearly 50 TB of synchronous multichannel recordings. The objective for this research is to do a preliminary analysis of the collected data and demonstrate influence of environmental factors (tidal, moon phase and daily period) on the orcas' acoustic activities.
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