Voice pathology detection using auto-correlation of different filters bank

This paper investigates the contribution of frequency bands for automatic voice pathology detection. First, the input voice signal is passed through a number of time-domain band-pass filters. The center frequencies are spaced on an octave scale. Each filter output is then divided into overlapping fr...

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Main Authors: Al-Nasheri, A., Ali, Z., Muhammad, G., Alsulaiman, M.
Format: Conference or Workshop Item
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.31704 /
Published: IEEE Computer Society 2014
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988239949&doi=10.1109%2fAICCSA.2014.7073178&partnerID=40&md5=bdedcb6044c0a83f39809c9ca544c2b2
http://eprints.utp.edu.my/31704/
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spelling utp-eprints.317042022-03-29T03:35:19Z Voice pathology detection using auto-correlation of different filters bank Al-Nasheri, A. Ali, Z. Muhammad, G. Alsulaiman, M. This paper investigates the contribution of frequency bands for automatic voice pathology detection. First, the input voice signal is passed through a number of time-domain band-pass filters. The center frequencies are spaced on an octave scale. Each filter output is then divided into overlapping frames. Auto-correlation function is applied to each block to find the first largest peak, in areas other than near the dc value, and its corresponding lag. Therefore, each frame is having only these two features (peak value and lag). As classifier, we use Gaussian mixture models (GMM) and support vector machine (SVM), separately. Two well-known available databases, one in English (MEEI) and the other one in German (SVD), are used in the investigation. The results demonstrate that the most significant frequency range to detect voice pathology is between 1500 Hz and 3500 Hz. Using this filter band and with only two features, the accuracy is above 97 in case of the MEEI database. © 2014 IEEE. IEEE Computer Society 2014 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988239949&doi=10.1109%2fAICCSA.2014.7073178&partnerID=40&md5=bdedcb6044c0a83f39809c9ca544c2b2 Al-Nasheri, A. and Ali, Z. and Muhammad, G. and Alsulaiman, M. (2014) Voice pathology detection using auto-correlation of different filters bank. In: UNSPECIFIED. http://eprints.utp.edu.my/31704/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description This paper investigates the contribution of frequency bands for automatic voice pathology detection. First, the input voice signal is passed through a number of time-domain band-pass filters. The center frequencies are spaced on an octave scale. Each filter output is then divided into overlapping frames. Auto-correlation function is applied to each block to find the first largest peak, in areas other than near the dc value, and its corresponding lag. Therefore, each frame is having only these two features (peak value and lag). As classifier, we use Gaussian mixture models (GMM) and support vector machine (SVM), separately. Two well-known available databases, one in English (MEEI) and the other one in German (SVD), are used in the investigation. The results demonstrate that the most significant frequency range to detect voice pathology is between 1500 Hz and 3500 Hz. Using this filter band and with only two features, the accuracy is above 97 in case of the MEEI database. © 2014 IEEE.
format Conference or Workshop Item
author Al-Nasheri, A.
Ali, Z.
Muhammad, G.
Alsulaiman, M.
spellingShingle Al-Nasheri, A.
Ali, Z.
Muhammad, G.
Alsulaiman, M.
Voice pathology detection using auto-correlation of different filters bank
author_sort Al-Nasheri, A.
title Voice pathology detection using auto-correlation of different filters bank
title_short Voice pathology detection using auto-correlation of different filters bank
title_full Voice pathology detection using auto-correlation of different filters bank
title_fullStr Voice pathology detection using auto-correlation of different filters bank
title_full_unstemmed Voice pathology detection using auto-correlation of different filters bank
title_sort voice pathology detection using auto-correlation of different filters bank
publisher IEEE Computer Society
publishDate 2014
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988239949&doi=10.1109%2fAICCSA.2014.7073178&partnerID=40&md5=bdedcb6044c0a83f39809c9ca544c2b2
http://eprints.utp.edu.my/31704/
_version_ 1741197621891956736
score 11.62408

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