Open AccessOPTIMIZATION OF DUAL-TREE COMPLEX WAVELET PACKET BASED ENTROPY FEATURES FOR VOICE PATHOLOGIES DETECTION
Author(s) -
Farah Nazlia Che Kassim,
Vikneswaran Vijean,
Zulkapli Abdullah,
M. Hariharan,
Rokiah Abdullah
Publication year - 2020
Publication title -
jurnal teknologi/jurnal teknologi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.191
H-Index - 22
eISSN - 2180-3722
pISSN - 0127-9696
DOI - 10.11113/jurnalteknologi.v82.14748
Subject(s) - pattern recognition (psychology) , support vector machine , computer science , complex wavelet transform , approximate entropy , artificial intelligence , wavelet packet decomposition , speech recognition , entropy (arrow of time) , wavelet , feature selection , network packet , wavelet transform , computer network , physics , quantum mechanics
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.