Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.12/5034
Título: Call recognition and individual identification of fish vocalizations based on automatic speech recognition: An example with the Lusitanian toadfish
Autor: Vieira, Manuel
Fonseca, Paulo Jorge
Amorim, Maria Clara Pessoa
Teixeira, Carlos J. C.
Palavras-chave: Animals
Markov Chains
Sound Spectrography
Speech Production Measurement
Pattern Recognition
Sexual Behavior
Signal Processing
Speech Acoustics
Data: 2015
Editora: Acoustical Society of America
Citação: Journal of the Acoustical Society of America, 138, 3941-3950. Doi: 10.1121/1.4936858
Resumo: 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.
Peer review: yes
URI: http://hdl.handle.net/10400.12/5034
DOI: 10.1121/1.4936858
ISSN: 0001-4966
Aparece nas colecções:MARE - Artigos em revistas internacionais

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