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  • Research Article
  • Open Access

Bird Species Recognition Using Support Vector Machines

EURASIP Journal on Advances in Signal Processing20072007:038637

  • Received: 13 November 2006
  • Accepted: 31 March 2007
  • Published:


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.


  • Support Vector Machine
  • Information Technology
  • Decision Tree
  • Support Vector
  • Quantum Information

Authors’ Affiliations

Laboratory of Acoustics and Audio Signal Processing, Helsinki University of Technology, P.O. Box 3000, 02015, TKK, Finland


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© 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.