Marine Sciences Institute, University of Quebec at Rimouski, 310 Allee des Ursulines, Rimouski, Quebec G5L-3A1, Canada.
J Acoust Soc Am. 2009 Dec;126(6):2918-28. doi: 10.1121/1.3257588.
Monitoring blue and fin whales summering in the St. Lawrence Estuary with passive acoustics requires call recognition algorithms that can cope with the heavy shipping noise of the St. Lawrence Seaway and with multipath propagation characteristics that generate overlapping copies of the calls. In this paper, the performance of three time-frequency methods aiming at such automatic detection and classification is tested on more than 2000 calls and compared at several levels of signal-to-noise ratio using typical recordings collected in this area. For all methods, image processing techniques are used to reduce the noise in the spectrogram. The first approach consists in matching the spectrogram with binary time-frequency templates of the calls (coincidence of spectrograms). The second approach is based on the extraction of the frequency contours of the calls and their classification using dynamic time warping (DTW) and the vector quantization (VQ) algorithms. The coincidence of spectrograms was the fastest method and performed better for blue whale A and B calls. VQ detected more 20 Hz fin whale calls but with a higher false alarm rate. DTW and VQ outperformed for the more variable blue whale D calls.
使用被动声学监测在圣劳伦斯河口夏季栖息的蓝鲸和长须鲸，需要能够处理圣劳伦斯海道繁忙航运噪音以及多径传播特征（会生成呼叫的重叠副本）的呼叫识别算法。在本文中，针对这种自动检测和分类的三种时频方法的性能在 2000 多个呼叫上进行了测试，并在使用该地区典型录音的几个信噪比水平上进行了比较。对于所有方法，都使用图像处理技术来减少声谱图中的噪声。第一种方法是将声谱图与呼叫的二进制时频模板（声谱图的吻合）进行匹配。第二种方法是基于提取呼叫的频率轮廓，并使用动态时间规整（DTW）和矢量量化（VQ）算法对其进行分类。声谱图吻合是最快的方法，对于蓝鲸 A 和 B 呼叫效果更好。VQ 检测到更多 20Hz 的长须鲸呼叫，但误报率更高。DTW 和 VQ 对更具变异性的蓝鲸 D 呼叫表现更好。
