Bird song recognition with hidden Markov models

dc.contributor.advisorSchwardt, L.
dc.contributor.authorVan der Merwe, Hugo Jacobusen_ZA
dc.contributor.otherStellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.
dc.descriptionThesis (MScEng (Electrical and Electronic Engineering))--Stellenbosch University, 2008.
dc.description.abstractAutomatic bird song recognition and transcription is a relatively new field. Reliable automatic recognition systems would be of great benefit to further research in ornithology and conservation, as well as commercially in the very large birdwatching subculture. This study investigated the use of Hidden Markov Models and duration modelling for bird call recognition. Through use of more accurate duration modelling, very promising results were achieved with feature vectors consisting of only pitch and volume. An accuracy of 51% was achieved for 47 calls from 39 birds, with the models typically trained from only one or two specimens. The ALS pitch tracking algorithm was adapted to bird song to extract the pitch. Bird song synthesis was employed to subjectively evaluate the features. Compounded Selfloop Duration Modelling was developed as an alternative duration modelling technique. For long durations, this technique can be more computationally efficient than Ferguson stacks. The application of approximate string matching to bird song was also briefly considered.en_ZA
dc.publisherStellenbosch : Stellenbosch University
dc.subjectBird song recognitionen_ZA
dc.subjectHidden Markov modelsen_ZA
dc.subjectComputer sound processingen_ZA
dc.subjectTune recognitionen_ZA
dc.subjectDissertations -- Electronic engineeringen_ZA
dc.subjectTheses -- Electronic engineeringen_ZA
dc.subject.otherElectrical and Electronic Engineeringen_ZA
dc.titleBird song recognition with hidden Markov modelsen_ZA
dc.rights.holderStellenbosch University

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