Open Access

Bird Species Recognition Using Support Vector Machines

EURASIP Journal on Advances in Signal Processing20072007:038637

https://doi.org/10.1155/2007/38637

Received: 13 November 2006

Accepted: 31 March 2007

Published: 22 May 2007

Abstract

Automatic identification of bird species by their vocalization is studied in this paper. Bird sounds are represented with two different parametric representations: (i) the mel-cepstrum parameters and (ii) a set of low-level signal parameters, both of which have been found useful for bird species recognition. Recognition is performed in a decision tree with support vector machine (SVM) classifiers at each node that perform classification between two species. Recognition is tested with two sets of bird species whose recognition has been previously tested with alternative methods. Recognition results with the proposed method suggest better or equal performance when compared to existing reference methods.

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Authors’ Affiliations

(1)
Laboratory of Acoustics and Audio Signal Processing, Helsinki University of Technology

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Copyright

© Seppo Fagerlund. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.