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Application of Wavelet Analysis and Paraconsistent Feature Extraction in the Classification of Voice Pathologies.

Gabriel José Pellisser Dalalana1, Rodrigo Capobianco Guido2, Eduardo Sperle Honorato3

  • 1Department of Electrical and Computer Engineering, School of Engineering of São Carlos, University of São Paulo, São Paulo, Brazil.

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Summary

This study introduces a novel method using wavelet analysis and paraconsistent logic for accurate voice pathology classification. The approach effectively distinguishes between healthy and disordered voices, offering a computationally efficient diagnostic tool.

Keywords:
Voice disorder classification—Wavelet analysis—Paraconsistent logic—Support vector machine—Non-invasive techniques

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Area of Science:

  • Signal Processing
  • Computational Intelligence
  • Speech Science

Background:

  • Accurate classification of voice pathologies is crucial for diagnosis and treatment.
  • Traditional methods may struggle with the inherent uncertainty and overlap in voice signal data.

Purpose of the Study:

  • To develop and evaluate a combined wavelet analysis and paraconsistent logic framework for voice pathology classification.
  • To identify optimal wavelet filters and features for distinguishing healthy from pathological voice samples.

Main Methods:

  • Voice signals were decomposed using discrete-time wavelet packet transform.
  • Features like energy and zero-crossing rate (ZCR) were extracted and classified using support vector machines.
  • A paraconsistent logic framework was employed to manage classification uncertainty.

Main Results:

  • The proposed methodology achieved high classification accuracy, outperforming existing state-of-the-art methods.
  • Specific wavelet filters (e.g., Sym32, Daub4, Haar) and features (energy, ZCR) showed superior performance for different pathologies.
  • Paraconsistent logic effectively handled class overlap and uncertainty.

Conclusions:

  • Wavelet analysis combined with paraconsistent logic provides a robust and efficient approach for voice pathology classification.
  • This integrated method shows promise for clinical applications and the development of real-time diagnostic tools.