01892nas 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