Vieira Manuel, Fonseca Paulo J, Amorim M Clara P, Teixeira Carlos J C
Departamento de Biologia Animal and cE3c - Centre for Ecology, Evolution and Environmental Changes, Faculdade de Ciências, Universidade de Lisboa, Bloco C2. Campo Grande, 1749-016 Lisboa, Portugal.
MARE-Marine and Environmental Sciences Centre, ISPA-Instituto Universitário, Rua Jardim do Tabaco 34, 1149-041 Lisboa, Portugal.
J Acoust Soc Am. 2015 Dec;138(6):3941-50. doi: 10.1121/1.4936858.
The study of acoustic communication in animals often requires not only the recognition of species specific acoustic signals but also the identification of individual subjects, all in a complex acoustic background. Moreover, when very long recordings are to be analyzed, automatic recognition and identification processes are invaluable tools to extract the relevant biological information. A pattern recognition methodology based on hidden Markov models is presented inspired by successful results obtained in the most widely known and complex acoustical communication signal: human speech. This methodology was applied here for the first time to the detection and recognition of fish acoustic signals, specifically in a stream of round-the-clock recordings of Lusitanian toadfish (Halobatrachus didactylus) in their natural estuarine habitat. The results show that this methodology is able not only to detect the mating sounds (boatwhistles) but also to identify individual male toadfish, reaching an identification rate of ca. 95%. Moreover this method also proved to be a powerful tool to assess signal durations in large data sets. However, the system failed in recognizing other sound types.
对动物声学通讯的研究通常不仅需要识别物种特有的声学信号，还需要在复杂的声学背景中识别个体对象。此外，当要分析非常长的录音时，自动识别和鉴定过程是提取相关生物信息的宝贵工具。受在最广为人知且复杂的声学通讯信号——人类语音中获得的成功结果启发，提出了一种基于隐马尔可夫模型的模式识别方法。该方法首次在此应用于鱼类声学信号的检测和识别，具体应用于在其自然河口栖息地对葡萄牙海蟾蜍（Halobatrachus didactylus）进行的全天候录音流中。结果表明，该方法不仅能够检测到交配声音（船哨声），还能够识别个体雄性海蟾蜍，识别率约为95%。此外，该方法还被证明是评估大数据集中信号持续时间的有力工具。然而，该系统在识别其他声音类型时失败了。
