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

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—AFSR, a novel approach for decreasing false positives and increasing models’ 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 < 6% false positives, and >90% precision for the other two species. AFSR’ 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.

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.021272702348nas a2200265 4500008004100000245008600041210006900127260001600196300001400212490000600226520155300232653001701785653002501802653001501827653001201842100001901854700002301873700002201896700002101918700002301939700002201962700002401984700002102008856005302029 2017 eng d00aMatlabHTK: a simple interface for bioacoustic analyses using hidden Markov models0 aMatlabHTK a simple interface for bioacoustic analyses using hidd cJan-05-2017 a615 - 6210 v83 a

1.Passive bioacoustic recording devices are now widely available and able to continuously record remotely located sites for extended periods, offering great potential for wildlife monitoring and management. Analysis of the huge data sets generated, in particular for specific biotic sound recognition, remains a critical bottleneck for widespread adoption of these technologies as current methods are labour intensive.

2. Several methods borrowed from speech processing frameworks, such as hidden Markov models, have been successful in analysing bioacoustic data, but the software implementations can be expensive and difficult to use for non-specialists involved in wildlife conservation. To remedy this, we present a software interface to a popular speech recognition system making it possible for non-experts to implement hidden Markov models for bioacoustic signal processing. Octave/Matlab functions are used to simplify the set-up and the definition of a bioacoustic signal recogniser as well as the analysis of the results.

3. We present the different functions as a workflow. To demonstrate how the package can be used, we give the results of an analysis of a bioacoustic monitoring data set to detect the nocturnal presence and behaviour of a cryptic seabird species, the common diving petrel Pelecanoides urinatrix urinatrix, from Northern New Zealand.


4. We show that the package MatlabHTK can be used efficiently to reconstruct the daily patterns of colony activity in the common diving petrel.

10abioacoustics10ahidden Markov models10amonitoring10aseabird1 aRanjard, Louis1 aReed, Benjamin, S.1 aLanders, Todd, J.1 aRayner, Matt, J.1 aFriesen, Megan, R.1 aSagar, Rachel, L.1 aDunphy, Brendon, J.1 aGaggiotti, Oscar uhttp://doi.wiley.com/10.1111/mee3.2017.8.issue-5