<?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%">Pessoa, Diogo</style></author><author><style face="normal" font="default" size="100%">Petrella, Lorena</style></author><author><style face="normal" font="default" size="100%">Castelo-Branco, Miguel</style></author><author><style face="normal" font="default" size="100%">Teixeira, César</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Henriques, Jorge</style></author><author><style face="normal" font="default" size="100%">Neves, Nuno</style></author><author><style face="normal" font="default" size="100%">de Carvalho, Paulo</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Automatic Segmentation of Ultrasonic Vocalizations in Rodents</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Rodents ultrasonic vocalizations</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal processing</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal segmentation</style></keyword><keyword><style  face="normal" font="default" size="100%">Spectral Entropy</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://link.springer.com/10.1007/978-3-030-31635-8</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><pub-location><style face="normal" font="default" size="100%">Cham</style></pub-location><isbn><style face="normal" font="default" size="100%">978-3-030-31634-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Ultrasonic vocalizations studies in rodents have increasingly drawn researchers attention as it have been considered a powerful tool to understand the animals behavior and their interactions in different social and environmental contexts.&lt;/p&gt;
&lt;p&gt;This paper presents an entropy-based algorithm for accurate and robust segmentation of mouse ultrasonic calls. Instead of using the conventional energy-based features, the spectral entropy is developed to identify the audio segments accurately. The new approach for mice calls detection has been able to detect up to 97% of the vocalizations.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pessoa, Diogo</style></author><author><style face="normal" font="default" size="100%">Petrella, Lorena</style></author><author><style face="normal" font="default" size="100%">Castelo-Branco, Miguel</style></author><author><style face="normal" font="default" size="100%">Teixeira, César</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Henriques, Jorge</style></author><author><style face="normal" font="default" size="100%">Neves, Nuno</style></author><author><style face="normal" font="default" size="100%">de Carvalho, Paulo</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Automatic Segmentation of Ultrasonic Vocalizations in Rodents</style></title><secondary-title><style face="normal" font="default" size="100%">XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://link.springer.com/10.1007/978-3-030-31635-8</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><pub-location><style face="normal" font="default" size="100%">Cham</style></pub-location><isbn><style face="normal" font="default" size="100%">978-3-030-31634-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Ultrasonic vocalizations studies in rodents have increasingly drawn researchers attention as it have been considered a powerful tool to understand the animals behavior and their interactions in different social and environmental contexts.&lt;/p&gt;
&lt;p&gt;This paper presents an entropy-based algorithm for accurate and robust segmentation of mouse ultrasonic calls. Instead of using the conventional energy-based features, the spectral entropy is developed to identify the audio segments accurately. The new approach for mice calls detection has been able to detect up to 97% of the vocalizations.&lt;/p&gt;
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