Rickwood Peter, Taylor Andrew
Building 6, University of Technology, Sydney, PO Box 123, Broadway, NSW 2007, Sydney, Australia.
J Acoust Soc Am. 2008 Mar;123(3):1763-72. doi: 10.1121/1.2836748.
This paper presents mathematical techniques for automatically extracting and analyzing bioacoustic signals. Automatic techniques are described for isolation of target signals from background noise, extraction of features from target signals and unsupervised classification (clustering) of the target signals based on these features. The only user-provided inputs, other than raw sound, is an initial set of signal processing and control parameters. Of particular note is that the number of signal categories is determined automatically. The techniques, applied to hydrophone recordings of humpback whales (Megaptera novaeangliae), produce promising initial results, suggesting that they may be of use in automated analysis of not only humpbacks, but possibly also in other bioacoustic settings where automated analysis is desirable.
