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Bird song recognition with hidden Markov models

Thesis (MScEng (Electrical and Electronic Engineering))--Stellenbosch University, 2008.

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Bibliographic Details
Main Author: Van der Merwe, Hugo Jacobus
Other Authors: Schwardt, L.
Format: Thesis
Language:English
Published: Stellenbosch : Stellenbosch University 2008
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access_status_str Open Access
author Van der Merwe, Hugo Jacobus
author2 Schwardt, L.
author_browse Schwardt, L.
Van der Merwe, Hugo Jacobus
author_facet Schwardt, L.
Van der Merwe, Hugo Jacobus
author_sort Van der Merwe, Hugo Jacobus
collection Thesis
dc_rights_str_mv Stellenbosch University
description Thesis (MScEng (Electrical and Electronic Engineering))--Stellenbosch University, 2008.
format Thesis
id oai:scholar.sun.ac.za:10019.1/2399
institution Stellenbosch University (South Africa)
language English
last_indexed 2026-06-10T12:40:56.191Z
license_str Other — see source repository
provenance_str_mv Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository
publishDate 2008
publishDateRange 2008
publishDateSort 2008
publisher Stellenbosch : Stellenbosch University
publisherStr Stellenbosch : Stellenbosch University
record_format dspace
source_str SUNScholar — Stellenbosch University Repository
spelling oai:scholar.sun.ac.za:10019.1/2399 Bird song recognition with hidden Markov models Van der Merwe, Hugo Jacobus Schwardt, L. Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering. Bird song recognition Hidden Markov models Computer sound processing Tune recognition Dissertations -- Electronic engineering Theses -- Electronic engineering Electrical and Electronic Engineering Thesis (MScEng (Electrical and Electronic Engineering))--Stellenbosch University, 2008. Automatic 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. 2008-06-23T13:43:03Z 2010-06-01T08:47:55Z 2008-06-23T13:43:03Z 2010-06-01T08:47:55Z 2008-03 Thesis http://hdl.handle.net/10019.1/2399 en Stellenbosch University application/pdf Stellenbosch : Stellenbosch University
spellingShingle Bird song recognition
Hidden Markov models
Computer sound processing
Tune recognition
Dissertations -- Electronic engineering
Theses -- Electronic engineering
Electrical and Electronic Engineering
Van der Merwe, Hugo Jacobus
Bird song recognition with hidden Markov models
title Bird song recognition with hidden Markov models
title_full Bird song recognition with hidden Markov models
title_fullStr Bird song recognition with hidden Markov models
title_full_unstemmed Bird song recognition with hidden Markov models
title_short Bird song recognition with hidden Markov models
title_sort bird song recognition with hidden markov models
topic Bird song recognition
Hidden Markov models
Computer sound processing
Tune recognition
Dissertations -- Electronic engineering
Theses -- Electronic engineering
Electrical and Electronic Engineering
url http://hdl.handle.net/10019.1/2399
work_keys_str_mv AT vandermerwehugojacobus birdsongrecognitionwithhiddenmarkovmodels