Cotillard Tristan, Sécheresse Xavier, Aubin Jaclyn, Mikus Marie-Ana, Vergara Valeria, Gambs Sébastien, Michaud Robert, Martins Cristiane C A, Turgeon Samuel, Chion Clément, Roca Irene
Department of Natural Sciences, Université du Quebec en Outaouais, Gatineau, Quebec, Canada.
Mines Paris, Paris Sciences et Lettres University, Paris, France.
J Acoust Soc Am. 2024 Dec 1;156(6):3723-3740. doi: 10.1121/10.0030472.
The endangered beluga whale (Delphinapterus leucas) of the St. Lawrence Estuary (SLEB) faces threats from a variety of anthropogenic factors. Since belugas are a highly social and vocal species, passive acoustic monitoring has the potential to deliver, in a non-invasive and continuous way, real-time information on SLEB spatiotemporal habitat use, which is crucial for their monitoring and conservation. In this study, we introduce an automatic pipeline to analyze continuous passive acoustic data and provide standard and accurate estimations of SLEB acoustic presence and vocal activity. An object detector extracted vocalizations of beluga whales from an acoustic recording of beluga vocal activity. Then, two deep learning classifiers discriminated between high-frequency call types (40-120 kHz) and the presence of low-frequency components (0-20 kHz), respectively. Different algorithms were tested for each step and their main combinations were compared in time and performance. We focused our work on a high residency area, Baie Sainte-Marguerite (BSM), used for socialization and feeding by SLEB. Overall, this project showed that accurate continuous analysis of SLEB vocal activity at BSM could provide valuable information to estimate habitat use, link beluga behavior and acoustic activity within and between herds, and quantify beluga presence and abundance.
圣劳伦斯河口（SLEB）濒危的白鲸（白鲸属）面临着多种人为因素的威胁。由于白鲸是高度群居且善于发声的物种，被动声学监测有潜力以非侵入性且持续的方式提供关于SLEB时空栖息地利用的实时信息，这对其监测和保护至关重要。在本研究中，我们引入了一个自动流程来分析连续的被动声学数据，并对白鲸的声学存在和发声活动提供标准且准确的估计。一个目标检测器从白鲸发声活动的声学记录中提取白鲸的叫声。然后，两个深度学习分类器分别区分高频叫声类型（40 - 120千赫）和低频成分（0 - 20千赫）的存在情况。对每个步骤测试了不同的算法，并在时间和性能方面比较了它们的主要组合。我们将工作重点放在了一个高栖息地利用区域——圣玛格丽特湾（BSM），这里是SLEB用于社交和觅食的地方。总体而言，该项目表明，对BSM白鲸发声活动进行准确的连续分析可为估计栖息地利用、关联鲸群内部和之间的白鲸行为与声学活动以及量化白鲸的存在和数量提供有价值的信息。
