Data from: MatlabHTK: a simple interface for bioacoustic analyses using Hidden Markov models
Cite this dataset
Reed, Benjamin S. et al. (2017). Data from: MatlabHTK: a simple interface for bioacoustic analyses using Hidden Markov models [Dataset]. Dryad. https://doi.org/10.5061/dryad.6kv00
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 datasets 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 dataset 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.