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Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/15126

Title: Neural network based detection of voice disorders using energy spectrum and equal-loudness contours
Authors: Murugesa Pandian, Paulraj, Prof. Madya Dr.
Sazali, Yaacob, Prof. Dr.
Sivanandam, S. N.
Muthusamy, Hariharan, Dr.
???metadata.dc.contributor.url???: paul@unimap.edu.my
Keywords: Band energy spectrum;Equal-loudness contours;Elman recurrent network
Issue Date: 7-Mar-2008
Publisher: Universiti Teknologi MARA (UiTM)
???metadata.dc.publisher.department???: Faculti of Electrical Engineering
Citation: p. 105-109
Series/Report no.: Proceedings 4th International Colloquium in Signal Processing and its Application 2008 (CSPA 2008)
Abstract: Impairment of vocal function can have a major impact on the quality of life, severely limiting communication at work and affecting all social aspect of daily life. In the recent years, voice disease are increasing dramatically due to unhealthy social habits and voice abuse. Acoustic analysis is a non-invasive technique to detect and diagnose the voice disorders. In this paper, a simple feature extraction method based on band energy spectrum and weighing factor of its center frequency derived from Equal-loudness contours is proposed. A simple Elman recurrent network models is developed for testing the proposed features. The simulation results indicate that the proposed algorithm can be distinguish the voice as pathological or non-pathological voice and provides the mean classification accuracy of above 90%. The proposed method has been potential for diagnosing the voice disorders.
Description: Organized by Universiti Teknologi MARA (UiTM) Shah Alam, 7th - 9th March 2008 at Royale Bintang Hotel, Kuala Lumpur, Malaysia.
URI: http://hdl.handle.net/123456789/15126
ISBN: 978-983-42747-9-5
Appears in Collections:Paulraj Murugesa Pandiyan, Prof. Dr.
M. Hariharan, Dr.
Conference Papers
Sazali Yaacob, Prof. Dr.

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