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An approach for automatic classification of grouper vocalizations with passive acoustic monitoring.
Ibrahim, Ali K; Chérubin, Laurent M; Zhuang, Hanqi; Schärer Umpierre, Michelle T; Dalgleish, Fraser; Erdol, Nurgun; Ouyang, B; Dalgleish, A.
Afiliação
  • Ibrahim AK; Department Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, Florida 33431, USA.
  • Chérubin LM; Harbor Branch Oceanographic Institute, Florida Atlantic University, 5600 US1 North, Fort Pierce, Florida 34946, USA.
  • Zhuang H; Department Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, Florida 33431, USA.
  • Schärer Umpierre MT; Department of Marine Sciences, University of Puerto Rico, Mayagüez 00681, Puerto Rico, USA.
  • Dalgleish F; Harbor Branch Oceanographic Institute, Florida Atlantic University, 5600 US1 North, Fort Pierce, Florida 34946, USA.
  • Erdol N; Department Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, Florida 33431, USA.
  • Ouyang B; Harbor Branch Oceanographic Institute, Florida Atlantic University, 5600 US1 North, Fort Pierce, Florida 34946, USA.
  • Dalgleish A; Harbor Branch Oceanographic Institute, Florida Atlantic University, 5600 US1 North, Fort Pierce, Florida 34946, USA.
J Acoust Soc Am ; 143(2): 666, 2018 02.
Article em En | MEDLINE | ID: mdl-29495690
Grouper, a family of marine fishes, produce distinct vocalizations associated with their reproductive behavior during spawning aggregation. These low frequencies sounds (50-350 Hz) consist of a series of pulses repeated at a variable rate. In this paper, an approach is presented for automatic classification of grouper vocalizations from ambient sounds recorded in situ with fixed hydrophones based on weighted features and sparse classifier. Group sounds were labeled initially by humans for training and testing various feature extraction and classification methods. In the feature extraction phase, four types of features were used to extract features of sounds produced by groupers. Once the sound features were extracted, three types of representative classifiers were applied to categorize the species that produced these sounds. Experimental results showed that the overall percentage of identification using the best combination of the selected feature extractor weighted mel frequency cepstral coefficients and sparse classifier achieved 82.7% accuracy. The proposed algorithm has been implemented in an autonomous platform (wave glider) for real-time detection and classification of group vocalizations.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: J Acoust Soc Am Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: J Acoust Soc Am Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Estados Unidos