Comparative Analysis between Wavelets for the Identification of Pathological Voices

  • Náthalee Cavalcanti
  • Sandro Silva
  • Adriano Bresolin
  • Heliana Bezerra
  • Ana Maria G. Guerreiro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6419)

Abstract

This study presents a comparative analysis of wavelets, in order to find a descriptor that provides a better classification of voice pathologies. Different types of Wavelet Packet Transform were used as a tool for feature extraction and Support Vector Machine (SVM) to classify vocal disorders. Tests were conducted with 23 wavelets types in two SVMs, the first using the strategy “one vs. all” to classify normal and pathological voices and the second, using the strategy “one vs. one” to classify pathologies: edema and nodules. The best results were obtained using Daubechies family, especially Daubechies 5 (db5) wavelet.

Keywords

Vocal disorder Wavelet Packet Transform Support Vector Machine (SVM) 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Náthalee Cavalcanti
    • 1
  • Sandro Silva
    • 1
  • Adriano Bresolin
    • 2
  • Heliana Bezerra
    • 1
  • Ana Maria G. Guerreiro
    • 1
  1. 1.UFRNFederal University of Rio grande do NorteBrazil
  2. 2.UTFPRFederal Technological University of ParanáBrazil

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