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/10.1121/1.502228101892nas a2200181   4500008004100000022001300041245012200054210006900176260001600245300001400261490000800275520128200283100002201565700001801587700002101605700001801626856006601644       2017                        eng d  a0003682X00aTwo-stage detection of north Atlantic right whale upcalls using local binary patterns and machine learning algorithms0 aTwostage detection of north Atlantic right whale upcalls using l  cJan-05-2017  a158 - 1660 v1203 a<p>In this paper, we investigate the effectiveness of two-stage classification strategies in detecting north Atlantic right whale upcalls. Time-frequency measurements of data from passive acoustic monitoring devices are evaluated as images. Vocalization spectrograms are preprocessed for noise reduction and tone removal. First stage of the algorithm eliminates non-upcalls by an energy detection algorithm. In the second stage, two sets of features are extracted from the remaining signals using contour-based and texture based methods. The former is based on extraction of time&ndash;frequency features from upcall contours, and the latter employs a Local Binary Pattern operator to extract distinguishing texture features of the upcalls. Subsequently evaluation phase is carried out by using several classifiers to assess the effectiveness of both the contour-based and texture-based features for upcall detection. Comparing ROC curves of machine learning algorithms obtained from Cornell University&rsquo;s dataset reveals that LBP features improved performance accuracy up to 43% over time&ndash;frequency features. Classifiers such as the Linear Discriminant Analysis, Support Vector Machine, and TreeBagger achieve highest upcall detection rates with LBP features.</p>
1 aEsfahanian, Mahdi1 aErdol, Nurgun1 aGerstein, Edmund1 aZhuang, Hanqi  uhttp://linkinghub.elsevier.com/retrieve/pii/S0003682X17300774