<?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%">Zhao, Zhao</style></author><author><style face="normal" font="default" size="100%">Zhang, Sai-hua</style></author><author><style face="normal" font="default" size="100%">Xu, Zhi-yong</style></author><author><style face="normal" font="default" size="100%">Kristen M. Bellisario</style></author><author><style face="normal" font="default" size="100%">Dai, Nian-hua</style></author><author><style face="normal" font="default" size="100%">Omrani, Hichem</style></author><author><style face="normal" font="default" size="100%">Bryan C. Pijanowski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automated bird acoustic event detection and robust species classification</style></title><secondary-title><style face="normal" font="default" size="100%">Ecological Informatics</style></secondary-title><short-title><style face="normal" font="default" size="100%">Ecological Informatics</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">automated acoustic event detection</style></keyword><keyword><style  face="normal" font="default" size="100%">autoregressive model</style></keyword><keyword><style  face="normal" font="default" size="100%">bioacoustic monitoring</style></keyword><keyword><style  face="normal" font="default" size="100%">Gaussian mixture model</style></keyword><keyword><style  face="normal" font="default" size="100%">robust bird species classification</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan-04-2017</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://linkinghub.elsevier.com/retrieve/pii/S157495411630231X</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;N on - invasive b ioacoustic monitoring is becoming increasingly popular for b iodiversity conservation. Two automated methods for acoustic classification of bird species currently used are frame - based methods, a model that uses Hidden Markov Models (HMMs), and event - based methods, a model consisting of descriptive measurements or restricted to tonal or harmonic vocalizations. In this work, we propose a new method for automated field recording anal ysis with improved automated segm entation and robust bird species classification . We used a Gaussian Mixture Model ( GMM ) - based frame selection with an event - energy - based sifting procedure that selected representative acoustic events . We employed a Mel , band - pass filter bank on each event &amp;rsquo; s spectrogram. T he output in each subband was parameterized by an autoregressive (AR) model , which result ed in a feature consisting of all model coefficients. Finally, a support vector machine (SVM) algorithm was used for classification . The significance o f the proposed method lies in the parameterized feature s depict ing the species - specific spectral pattern . This experiment used a control audio dataset and real - world audio dataset comprised of field recordings of eleven bird species from the X eno - canto A rc hive , consisting of 2762 bird acoustic events with 339 detected &amp;ldquo; unknown &amp;rdquo; events (corresponding to noise or unknown species vocalization s) . Compared with other recent approach es , our proposed method provide s comparable identification performance with respe ct to the eleven species of interest . Meanwhile, superior robu stness in real - world scenarios is achieved, which is expressed as the considerable improvement from 0.632 to 0.928 for the F - score metric regarding the &amp;ldquo; unknown &amp;rdquo; events . The advantage makes the proposed method more suitable for automated field recording analysis.&lt;/p&gt;
</style></abstract></record></records></xml>