Computer-aided classification of bowhead whale call categories for mitigation monitoring

D. Mathias, A. Thode, S. B. Blackwell, C. Greene
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引用次数: 5

Abstract

Since 2001 Directional Autonomous Seafloor Acoustic Recorders (DASARs) have been used to localize and record bowhead whale (Balaena mysticetus) calls during their annual migration. In 2007 DASARs were deployed at 35 locations over a 280 km swath in the Beaufort Sea, during seismic exploration activities (Fig. 1), in order to monitor potential changes in the animals' location and/or acoustic activity during the seismic activities. The large amount of acoustic data generated (about 50 days per DASAR) motivated the development of computer-aided methods to assist in detecting and classifying bowhead whale calls. Bowhead whale calls can be classified in various ways. Here, we divide calls into six categories: (1) upsweeps, (2) downsweeps, (3) constant calls, (4) u-shaped and (5) n-shaped undulated calls, and (6) complex calls, a catch-all category that covers both frequency-modulated calls with multiple inflections, and amplitude-modulated calls such as warbles, growls, and other such sounds. In addition, walrus and bearded seal calls can produce similar call features in a spectrogram, yielding a total of eight classification categories. The frequency range, duration, and fine structure of individual calls vary considerably even within each category, creating difficulties when using simple matched- filtering or spectrogram correlation methods. A manually reviewed test dataset was assembled, containing examples from each call category, arranged by signal-to-noise ratio (SNR) in 5 dB bins, ranging from 5 to 40 dB. The dataset was then used to test several methods for extracting relevant parameters from the signal for subsequent classification. Contour tracing methods that estimate frequency bandwidth, inflection points, and duration were examined, as well as other boundary descriptors that utilize standard image segmentation techniques. An optimization procedure was then used to determine appropriate decision boundaries for optimum statistical classifiers.
缓解监测用弓头鲸叫声分类的计算机辅助分类
自2001年以来,定向自主海底声学记录仪(DASARs)被用于定位和记录弓头鲸(Balaena mysticetus)在年度迁徙期间的叫声。2007年,在地震勘探活动期间,在波弗特海280公里范围内的35个地点部署了DASARs(图1),以监测地震活动期间动物位置和/或声学活动的潜在变化。产生的大量声学数据(每个DASAR大约50天)推动了计算机辅助方法的发展,以帮助探测和分类弓头鲸的叫声。弓头鲸的叫声可以用不同的方式分类。在这里,我们将叫声分为六类:(1)向上扫描,(2)向下扫描,(3)恒定的叫声,(4)u形和(5)n形波动的叫声,以及(6)复杂的叫声,这是一个全面的类别,涵盖了多种音调的调频叫声和调幅的叫声,如颤音、咆哮和其他类似的声音。此外,海象和胡须海豹的叫声可以在频谱图中产生相似的叫声特征,从而产生总共八个分类类别。即使在每个类别中,单个呼叫的频率范围、持续时间和精细结构也有很大差异,这在使用简单的匹配滤波或频谱图相关方法时造成了困难。我们组装了一个人工审查的测试数据集,其中包含来自每个呼叫类别的示例,按信噪比(SNR)排列在5 dB的箱子中,范围从5到40 dB。然后使用该数据集测试几种从信号中提取相关参数的方法,以进行后续分类。研究了估计频率带宽、拐点和持续时间的轮廓跟踪方法,以及利用标准图像分割技术的其他边界描述符。然后使用优化程序确定最佳统计分类器的适当决策边界。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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