<?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%">Campos, Ivan Braga</style></author><author><style face="normal" font="default" size="100%">Landers, Todd J.</style></author><author><style face="normal" font="default" size="100%">Lee, Kate D.</style></author><author><style face="normal" font="default" size="100%">Lee, William George</style></author><author><style face="normal" font="default" size="100%">Friesen, Megan R.</style></author><author><style face="normal" font="default" size="100%">Gaskett, Anne C.</style></author><author><style face="normal" font="default" size="100%">Ranjard, Louis</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Dennis M. Higgs</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Assemblage of Focal Species Recognizers—AFSR: A technique for decreasing false indications of presence from acoustic automatic identification in a multiple species context</style></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%">https://dx.plos.org/10.1371/journal.pone.0212727</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;Passive acoustic monitoring (PAM) coupled with automated species identification is a prom- ising tool for species monitoring and conservation worldwide. However, high false indica- tions of presence are still an important limitation and a crucial factor for acceptance of these techniques in wildlife surveys. Here we present the Assemblage of Focal Species Recogniz- ers&amp;mdash;AFSR, a novel approach for decreasing false positives and increasing models&amp;rsquo; preci- sion in multispecies contexts. AFSR focusses on decreasing false positives by excluding unreliable sound file segments that are prone to misidentification. We used MatlabHTK, a hidden Markov models interface for bioacoustics analyses, for illustrating AFSR technique by comparing two approaches, 1) a multispecies recognizer where all species are identified simultaneously, and 2) an assemblage of focal species recognizers (AFSR), where several recognizers that each prioritise a single focal species are then summarised into a single out- put, according to a set of rules designed to exclude unreliable segments. Both approaches (the multispecies recognizer and AFSR) used the same sound files training dataset, but dif- ferent processing workflow. We applied these recognisers to PAM recordings from a remote island colony with five seabird species and compared their outputs with manual species identifications. False positives and precision improved for all the five species when using AFSR, achieving remarkable 0% false positives and 100% precision for three of five seabird species, and &amp;lt; 6% false positives, and &amp;gt;90% precision for the other two species. AFSR&amp;rsquo; out- put was also used to generate daily calling activity patterns for each species. Instead of attempting to withdraw useful information from every fragment in a sound recording, AFSR prioritises more trustworthy information from sections with better quality data. AFSR can be applied to automated species identification from multispecies PAM recordings worldwide.&lt;/p&gt;
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