<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Esfahanian, Mahdi</style></author><author><style face="normal" font="default" size="100%">Erdol, Nurgun</style></author><author><style face="normal" font="default" size="100%">Gerstein, Edmund</style></author><author><style face="normal" font="default" size="100%">Zhuang, Hanqi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Two-stage detection of north Atlantic right whale upcalls using local binary patterns and machine learning algorithms</style></title><secondary-title><style face="normal" font="default" size="100%">Applied Acoustics</style></secondary-title><short-title><style face="normal" font="default" size="100%">Applied Acoustics</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan-05-2017</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://linkinghub.elsevier.com/retrieve/pii/S0003682X17300774</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">120</style></volume><pages><style face="normal" font="default" size="100%">158 - 166</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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&amp;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&amp;rsquo;s dataset reveals that LBP features improved performance accuracy up to 43% over time&amp;ndash;frequency features. Classifiers such as the Linear Discriminant Analysis, Support Vector Machine, and TreeBagger achieve highest upcall detection rates with LBP features.&lt;/p&gt;
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