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.021272701392nas a2200241   4500008004100000245005800041210005700099520070900156653001800865653002800883653001700911653001900928653002300947653001700970653001500987653002101002100002001023700001401043700001901057700001901076700001701095856003801112       2019                        eng d00aNIPS4Bplus: a richly annotated birdsong audio dataset0 aNIPS4Bplus a richly annotated birdsong audio dataset3 a<p>Recent advances in birdsong detection and classification have approached a limit due to the lack of fully annotated recordings. In this paper, we present NIPS4Bplus, the first richly annotated birdsong audio dataset, that is comprised of recordings containing bird vocalisations along with their active species tags plus the temporal annotations acquired for them. Statistical information about the recordings, their species specific tags and their temporal annotations are presented along with example uses. NIPS4Bplus could be used in various ecoacoustic tasks, such as training models for bird population monitoring, species classification, birdsong vocalisation detection and classification.</p>
10aaudio dataset10aAudio signal processing10abioacoustics10aBioinformatics10aBird vocalisations10aecoacoustics10aecosystems10arich annotations1 aMorfi, Veronica1 aBas, Yves1 aPamuła, Hanna1 aGlotin, Hervé1 aStowell, Dan  uhttps://peerj.com/articles/cs-223