OPTIMIZATION OF DUAL-TREE COMPLEX WAVELET PACKET BASED ENTROPY FEATURES FOR VOICE PATHOLOGIES DETECTION

Farah Nazlia Che Kassim; Vikneswaran Vijean; Zulkapli Abdullah; Hariharan Muthusamy; Rokiah Abdullah

doi:10.11113/jurnalteknologi.v82.14748

Authors

Farah Nazlia Che Kassim Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia
Vikneswaran Vijean Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia
Zulkapli Abdullah Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia
Hariharan Muthusamy Department of Electronic Engineering, National Institute of Technology, Srinagar (Garhwal), Uttarakhand India
Rokiah Abdullah Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia

DOI:

https://doi.org/10.11113/jurnalteknologi.v82.14748

Keywords:

DT-CWPT, Feature Extraction, Feature Selection, Voice Pathologies

Abstract

The Dual-Tree Complex Wavelet Packet Transform (DT-CWPT) has been successfully implemented in numerous field because it introduces limited redundancy, provides approximately shift-invariance and geometrically oriented signal in multiple dimensions where these properties are lacking in traditional wavelet transform. This paper investigates the performance of features extracted using DT-CWPT algorithms which are quantified using k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM) classifiers for detecting voice pathologies. Decomposition is done on the voice signals using Shannon and Approximate entropy (ApEn) to signify the complexity of voice signals in time and frequency domain. Feature selection methods using the ReliefF algorithm and Genetic algorithm (GA) are applied to obtain the optimum features for multiclass classification. It is observed that the best accuracies obtained using DT-CWPT with ApEn entropy are 91.15 % for k-NN and 93.90 % for SVM classifiers. The proposed work provides a promising detection rate for multiple voice disorders and is useful for the development of computer-based diagnostic tools for voice pathology screening in health care facilities.

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