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Voice Pathologies Classification and Detection Using EMD-DWT Analysis Based on Higher Order Statistic Features
Institution:1. Digital Speech Processing Group, Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia;2. Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Tronoh 31750, Perak, Malaysia;3. ENT Department, College of Medicine, King Saud University, Riyadh, Saudi Arabia;4. Research Chair of Voice, Swallowing, and Communication Disorders, King Saud University, Riyadh, Saudi Arabia;5. ENT Department, College of Medicine, Menoufiya University, Shebin Alkoum, Egypt;1. Department of Computer Engineering, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran;2. Department of Electrical Engineering, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran;1. School of Information Science and Engineering, Yanshan University, Qinhuangdao, PR China;2. Hebei Key Laboratory on Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, PR China;1. Instituto de Biociências, Letras e Ciências Exatas, Unesp - Univ Estadual Paulista (São Paulo State University), Rua Cristóvão Colombo 2265, Jd Nazareth, 15054-000, São José do Rio Preto, SP, Brazil;2. IFSP - São Paulo Federal Institute, Department of Industry and Automation, Catanduva, SP, Brazil;3. UEL - Londrina State University, Computer Science Department, Londrina, PR, Brazil;4. FATEC - São Paulo State Technology College, S. J. Rio Preto, SP, Brazil;1. Universidad Politécnica de Madrid, Ctra. Valencia, km. 7, 28031 Madrid, Spain;2. Johns Hopkins University, Baltimore, MD 21218, USA
Abstract:BackgroundThe voice is a prominent tool allowing people to communicate and to change information in their daily activities. However, any slight alteration in the voice production system may affect the voice quality. Over the last years, researchers in biomedical engineering field worked to develop a robust automatic system that may help clinicians to perform a preventive diagnosis in order to detect the voice pathologies in an early stage.MethodIn this context, pathological voice detection and classification method based on EMD-DWT analysis and Higher Order Statistics (HOS) features, is proposed. Also DWT coefficients features are extracted and tested. To carry out our experiments a wide subset of voice signal from normal subjects and subjects which suffer from the five most frequent pathologies in the Saarbrücken Voice Database (SVD), is selected. In The first step, we applied the Empirical Mode Decomposition (EMD) to the voice signal. Afterwards, among the obtained candidates of Intrinsic Mode Functions (IMFs), we choose the robust one based on temporal energy criterion. In the second step, the selected IMF was decomposed via the Discrete Wavelet Transform (DWT). As a result, two features vector includes six HOSs parameters, and a features vector includes six DWT features were formed from both approximation and detail coefficients. In order to classify the obtained data a support vector machine (SVM) is employed. After having trained the proposed system using the SVD database, the system was evaluated using voice signals of volunteer's subjects from the Neurological department of RABTA Hospital of Tunis.ResultsThe proposed method gives promising results in pathological voices detection. The accuracies reached 99.26% using HOS features and 93.1% using DWT features for SVD database. In the classification, an accuracy of 100% was reached for “Funktionelle Dysphonia vs. Rekrrensparese” based on HOS features. Nevertheless, using DWT features the accuracy achieved was 90.32% for “Hyperfunktionelle Dysphonia vs. Rekurrensparse”. Furthermore, in the validation the accuracies reached were 94.82%, 91.37% for HOS and DWT features, respectively. In the classification the highest accuracies reached were for classifying “Parkinson versus Paralysis” 94.44% and 88.87% based on HOS and DWT features, respectively.ConclusionHOS features show promising results in the automatic voice pathology detection and classification compared to DWT features. Thus, it can reliably be used as noninvasive tool to assist clinical evaluation for pathological voices identification.
Keywords:EMD-DWT analysis  High Order Statistics features (HOS)  Pathological voices detection and classification  Saarbrücken Voice Database (SVD)  Support vector machine (SVM)
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