02728nas a2200181   4500008004100000245017800041210007100219520202800290100002402318700002202342700001802364700002502382700002302407700002202430700001902452700002202471856005302493       2019                        eng d00aAssemblage of Focal Species Recognizers—AFSR: A technique for decreasing false indications of presence from acoustic automatic identification in a multiple species context0 aAssemblage of Focal Species Recognizers—AFSR A technique for dec3 a<p>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&mdash;AFSR, a novel approach for decreasing false positives and increasing models&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 &lt; 6% false positives, and &gt;90% precision for the other two species. AFSR&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.</p>
1 aCampos, Ivan, Braga1 aLanders, Todd, J.1 aLee, Kate, D.1 aLee, William, George1 aFriesen, Megan, R.1 aGaskett, Anne, C.1 aRanjard, Louis1 aHiggs, Dennis, M.  uhttps://dx.plos.org/10.1371/journal.pone.0212727