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Bird Species Recognition Using Support Vector Machines

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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Correspondence to Seppo Fagerlund.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://doi.org/creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Fagerlund, S. Bird Species Recognition Using Support Vector Machines. EURASIP J. Adv. Signal Process. 2007, 038637 (2007). https://doi.org/10.1155/2007/38637

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Keywords

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