Socheleau Francois-Xavier, Leroy Emmanuelle, Pecci Andres Carvallo, Samaran Flore, Bonnel Julien, Royer Jean-Yves
Institut Mines-Telecom, Telecom Bretagne, Unités Mixtes de Recherche, Centre National de la Recherche Scientifique 6285, Laboratoire des Sciences et Techniques de l'Information, de la Communication et de la Connaissance, Technopôle Brest Iroise, CS 83818, 29238 Brest Cedex, France.
University of Brest, Centre National de la Recherche Scientifique, Laboratoire Domaines Océaniques, Institut Universitaire Européen de la Mer, 29280 Plouzané, France.
J Acoust Soc Am. 2015 Nov;138(5):3105-17. doi: 10.1121/1.4934271.
This paper addresses the problem of automated detection of Z-calls emitted by Antarctic blue whales (B. m. intermedia). The proposed solution is based on a subspace detector of sigmoidal-frequency signals with unknown time-varying amplitude. This detection strategy takes into account frequency variations of blue whale calls as well as the presence of other transient sounds that can interfere with Z-calls (such as airguns or other whale calls). The proposed method has been tested on more than 105 h of acoustic data containing about 2200 Z-calls (as found by an experienced human operator). This method is shown to have a correct-detection rate of up to more than 15% better than the extensible bioacoustic tool package, a spectrogram-based correlation detector commonly used to study blue whales. Because the proposed method relies on subspace detection, it does not suffer from some drawbacks of correlation-based detectors. In particular, it does not require the choice of an a priori fixed and subjective template. The analytic expression of the detection performance is also derived, which provides crucial information for higher level analyses such as animal density estimation from acoustic data. Finally, the detection threshold automatically adapts to the soundscape in order not to violate a user-specified false alarm rate.
本文探讨了自动检测南极蓝鲸（B. m. intermedia）发出的Z叫声的问题。所提出的解决方案基于一种用于检测具有未知时变幅度的S形频率信号的子空间检测器。这种检测策略考虑了蓝鲸叫声的频率变化以及可能干扰Z叫声的其他瞬态声音（如气枪声或其他鲸鱼叫声）的存在。所提出的方法已在超过105小时的声学数据上进行了测试，这些数据包含约2200次Z叫声（由经验丰富的人工操作员识别）。结果表明，该方法的正确检测率比可扩展生物声学工具包高出15%以上，可扩展生物声学工具包是一种常用于研究蓝鲸的基于频谱图的相关检测器。由于所提出的方法依赖于子空间检测，因此它没有基于相关性的检测器的一些缺点。特别是，它不需要选择先验固定且主观的模板。还推导了检测性能的解析表达式，这为诸如从声学数据估计动物密度等高级分析提供了关键信息。最后，检测阈值会自动适应声景，以不违反用户指定的误报率。
