<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Liu, Songzuo</style></author><author><style face="normal" font="default" size="100%">Liu, Meng</style></author><author><style face="normal" font="default" size="100%">Wang, Mengjia</style></author><author><style face="normal" font="default" size="100%">Ma, Tianlong</style></author><author><style face="normal" font="default" size="100%">Qing, Xin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Classification of Cetacean Whistles Based on Convolutional Neural Network</style></title><secondary-title><style face="normal" font="default" size="100%">2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/8555732/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Hangzhou, China</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Vocal communication is a primary communication method of cetaceans. Learning the language of them is of great significance for the protection of cetaceans, and it also provides support for acoustic study of cetaceans&amp;rsquo; biological behavior. In this study, a classification method based on deep learning is proposed for the classification of cetacean whistles. Firstly, the method performs short-time Fourier transform on whistles to obtain the time-frequency distribution, and then uses the deep learning model, convolutional neural network, to classify the signal based on time-frequency characteristics. The simulation results show that this method has a relatively high classification accuracy when choosing the appropriate parameters, indicating the method is effective.&lt;/p&gt;
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