02348nas 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