eprintid: 9428
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/94/28
datestamp: 2023-11-09 16:21:25
lastmod: 2023-11-09 16:21:25
status_changed: 2023-11-09 16:15:06
type: article
metadata_visibility: show
creators_name: Muhammad, G.
creators_name: Alsulaiman, M.
creators_name: Ali, Z.
creators_name: Mesallam, T.A.
creators_name: Farahat, M.
creators_name: Malki, K.H.
creators_name: Al-nasheri, A.
creators_name: Bencherif, M.A.
title: Voice pathology detection using interlaced derivative pattern on glottal source excitation
ispublished: pub
keywords: Database systems; Pathology; Singular value decomposition; Support vector machines, AVPD; Glottal source; Interlaced derivative pattern (IDP); MEEI; Voice pathology detection, Speech recognition, Article; automatic speech recognition; calculation; data base; excitation; filtration; glottal source excitation; human; interlaced derivative pattern; priority journal; signal noise ratio; support vector machine; vocal cord; voice; voice disorder
note: cited By 78
abstract: In this paper, we propose a voice pathology detection and classification method using an interlaced derivative pattern (IDP), which involves an n-th order directional derivative, on a spectro-temporal description of a glottal source excitation signal. It is shown previously that directional information is useful to detect pathologies due to its encoding ability along time, frequency, and time-frequency axes. The IDP, being an n-th order derivative, is capable of describing more information than a first order derivative pattern by combining all the directional information into one. In the IDP, first-order derivatives are calculated in four directions, and these derivatives are thresholded with the center value of each directional channel to produce the final IDP. A support vector machine is used as a classification technique. Experiments are conducted using three different databases, which are the Massachusetts Eye and Ear Infirmary database, Saarbrucken Voice Database, and Arabic Voice Pathology Database. Experimental results show that the IDP based features give higher accuracy than that using other related features in all the three databases. The accuracies using cross-databases are also high using the IDP features. © 2016 Elsevier Ltd
date: 2017
publisher: Elsevier Ltd
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84990178804&doi=10.1016%2fj.bspc.2016.08.002&partnerID=40&md5=95d1785e54fc7f127392121d1838f383
id_number: 10.1016/j.bspc.2016.08.002
full_text_status: none
publication: Biomedical Signal Processing and Control
volume: 31
pagerange: 156-164
refereed: TRUE
issn: 17468094
citation:   Muhammad, G. and Alsulaiman, M. and Ali, Z. and Mesallam, T.A. and Farahat, M. and Malki, K.H. and Al-nasheri, A. and Bencherif, M.A.  (2017) Voice pathology detection using interlaced derivative pattern on glottal source excitation.  Biomedical Signal Processing and Control, 31.  pp. 156-164.  ISSN 17468094