01962nas a2200229   4500008004100000022001400041245010300055210006900158260001600227300001400243490000800257520124200265100002101507700002701528700001801555700003501573700002201608700001801630700001501648700001801663856005101681       2018                        eng d  a0001-496600aAn approach for automatic classification of grouper vocalizations with passive acoustic monitoring0 aapproach for automatic classification of grouper vocalizations w  cJan-02-2018  a666 - 6760 v1433 a<p>Grouper, a family of marine fishes, produce distinct vocalizations associated with their reproductive behavior during spawning aggregation. These low frequencies sounds (50&ndash;350 Hz) consist of a series of pulses repeated at a variable rate. In this paper, an approach is presented for automatic classifica- tion 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 spe- cies that produced these sounds. Experimental results showed that the overall percentage of identifi- cation 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 imple- mented in an autonomous platform (wave glider) for real-time detection and classification of group vocalizations.</p>
1 aIbrahim, Ali, K.1 aChérubin, Laurent, M.1 aZhuang, Hanqi1 aUmpierre, Michelle, T. Schäre1 aDalgleish, Fraser1 aErdol, Nurgun1 aOuyang, B.1 aDalgleish, A.  uhttp://asa.scitation.org/doi/10.1121/1.5022281