Lin Tzu-Hao, Chou Lien-Siang
Institute of Ecology and Evolutionary Biology, National Taiwan University, Number 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan.
J Acoust Soc Am. 2015 Aug;138(2):1003-11. doi: 10.1121/1.4927695.
Classification of odontocete species remains a challenging task for passive acoustic monitoring. Classifiers that have been developed use spectral features extracted from echolocation clicks and whistle contours. Most of these contour-based classifiers require complete contours to reduce measurement errors. Therefore, overlapping contours and partially detected contours in an automatic detection algorithm may increase the bias for contour-based classifiers. In this study, classification was conducted on each recording section without extracting individual contours. The local-max detector was used to extract representative frequencies of delphinid whistles and each section was divided into multiple non-overlapping fragments. Three acoustical parameters were measured from the distribution of representative frequencies in each fragment. By using the statistical features of the acoustical parameters and the percentage of overlapping whistles, correct classification rate of 70.3% was reached for the recordings of seven species (Tursiops truncatus, Delphinus delphis, Delphinus capensis, Peponocephala electra, Grampus griseus, Stenella longirostris longirostris, and Stenella attenuata) archived in MobySound.org. In addition, correct classification rate was not dramatically reduced in various simulated noise conditions. This algorithm can be employed in acoustic observatories to classify different delphinid species and facilitate future studies on the community ecology of odontocetes.
对于被动声学监测而言，齿鲸物种的分类仍然是一项具有挑战性的任务。已开发的分类器使用从回声定位咔哒声和啸叫声轮廓中提取的频谱特征。这些基于轮廓的分类器大多需要完整的轮廓以减少测量误差。因此，自动检测算法中重叠的轮廓和部分检测到的轮廓可能会增加基于轮廓的分类器的偏差。在本研究中，对每个录音片段进行分类，而不提取单个轮廓。使用局部最大值检测器提取海豚科啸叫声的代表性频率，并将每个片段划分为多个不重叠的片段。从每个片段中代表性频率的分布测量三个声学参数。通过使用声学参数的统计特征和重叠啸叫声的百分比，对于存档于MobySound.org的七种物种（宽吻海豚、真海豚、南非长吻真海豚、瓜头鲸、虎鲸、长吻飞旋原海豚和热带点斑原海豚）的录音，正确分类率达到了70.3%。此外，在各种模拟噪声条件下，正确分类率并未显著降低。该算法可用于声学观测站，以对不同的海豚科物种进行分类，并促进未来关于齿鲸群落生态学的研究。
