Bird Species Recognition Using Support Vector Machines

Research output: Contribution to journalArticleScientificpeer-review

Researchers

  • Seppo Fagerlund

Research units

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.

Details

Original languageEnglish
Article number038637
Pages (from-to)1-8
JournalEurasip Journal on Advances in Signal Processing
Volume2007
Publication statusPublished - 2007
MoE publication typeA1 Journal article-refereed

    Research areas

  • bird song, feature extraction, pattern recognition, species recognition, support, vector machine (SVM)

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