Cover Image

Optimal selection of long time acoustic features using GA for the assessment of vocal fold disorders

Link to publisher's homepage at http://www.ttp.net/

Saved in:
Bibliographic Details
Main Authors: Sindhu, Ravindran, Neoh, Siew Chin, Dr., Muthusamy, Hariharan, Dr.
Other Authors: jay.sayaang@gmail.com
Format: Article
Language:English
Published: Trans Tech Publications 2013
Subjects:
Feature selection
Genetic algorithm
K-NN classifier
Vocal fold problem
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/23811
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.unimap-23811
record_format dspace
spelling my.unimap-238112013-02-25T06:50:11Z Optimal selection of long time acoustic features using GA for the assessment of vocal fold disorders Sindhu, Ravindran Neoh, Siew Chin, Dr. Muthusamy, Hariharan, Dr. jay.sayaang@gmail.com scneoh@unimap.edu.my hari@unimap.edu.my Feature selection Genetic algorithm K-NN classifier Vocal fold problem Link to publisher's homepage at http://www.ttp.net/ In recent times, vocal fold problems have been increasing dramatically due to unhealthy social habits and voice abuse. Non-invasive methods like acoustic analysis of voice signals can be used to investigate such problems. Various feature extraction techniques are used to classify the voice signals into normal and pathological. Among them, long-time acoustical parameters are used by many researchers. The selection of best long-time acoustical parameters is very important to reduce the computational complexity, as well as to achieve better accuracy with minimum number of features. In order to select best long-time acoustical parameters, different feature reduction methods or feature selection methods are proposed by researchers. In this work, genetic algorithm (GA) based optimal selection of long-time acoustical parameters is proposed to achieve higher accuracy with minimum number of features. The classification is carried out using k-nearest neighbourhood (k-NN) classifier. In comparison with other works in the literature, the simulation results show that a minimum of 5 features are required to classify the voice signals by GA and a better accuracy of 94.29% is achieved. 2013-02-25T06:50:11Z 2013-02-25T06:50:11Z 2013 Article Applied Mechanics and Materials, vol. 39-240, 2013, pages 65-70 1660-9336 http://www.scientific.net/AMM.239-240.65 http://hdl.handle.net/123456789/23811 en Trans Tech Publications
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Feature selection
Genetic algorithm
K-NN classifier
Vocal fold problem
spellingShingle Feature selection
Genetic algorithm
K-NN classifier
Vocal fold problem
Sindhu, Ravindran
Neoh, Siew Chin, Dr.
Muthusamy, Hariharan, Dr.
Optimal selection of long time acoustic features using GA for the assessment of vocal fold disorders
description Link to publisher's homepage at http://www.ttp.net/
author2 jay.sayaang@gmail.com
author_facet jay.sayaang@gmail.com
Sindhu, Ravindran
Neoh, Siew Chin, Dr.
Muthusamy, Hariharan, Dr.
format Article
author Sindhu, Ravindran
Neoh, Siew Chin, Dr.
Muthusamy, Hariharan, Dr.
author_sort Sindhu, Ravindran
title Optimal selection of long time acoustic features using GA for the assessment of vocal fold disorders
title_short Optimal selection of long time acoustic features using GA for the assessment of vocal fold disorders
title_full Optimal selection of long time acoustic features using GA for the assessment of vocal fold disorders
title_fullStr Optimal selection of long time acoustic features using GA for the assessment of vocal fold disorders
title_full_unstemmed Optimal selection of long time acoustic features using GA for the assessment of vocal fold disorders
title_sort optimal selection of long time acoustic features using ga for the assessment of vocal fold disorders
publisher Trans Tech Publications
publishDate 2013
url http://dspace.unimap.edu.my/xmlui/handle/123456789/23811
_version_ 1643794059003691008
score 13.214268

Similar Items