| Summary: | 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.
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