Voice Disorder Classification Based on Multitaper Mel Frequency Cepstral Coefficients Features

The Mel Frequency Cepstral Coefficients (MFCCs) are widely used in order to extract essential information from a voice signal and became a popular feature extractor used in audio processing. However, MFCC features are usually calculated from a single window (taper) characterized by large variance. T...

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Main Authors: Ömer Eskidere, Ahmet Gürhanlı
Format: Article
Language:English
Published: Hindawi Limited 2015-01-01
Series:Computational and Mathematical Methods in Medicine
Online Access:http://dx.doi.org/10.1155/2015/956249
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spelling doaj-db138da9e88d4bcbbb0c87138f9ea8592020-11-25T01:28:57ZengHindawi LimitedComputational and Mathematical Methods in Medicine1748-670X1748-67182015-01-01201510.1155/2015/956249956249Voice Disorder Classification Based on Multitaper Mel Frequency Cepstral Coefficients FeaturesÖmer Eskidere0Ahmet Gürhanlı1Department of Electrical Electronics Engineering, Bursa Orhangazi University, 16310 Bursa, TurkeyDepartment of Computer Engineering, Bursa Orhangazi University, 16310 Bursa, TurkeyThe Mel Frequency Cepstral Coefficients (MFCCs) are widely used in order to extract essential information from a voice signal and became a popular feature extractor used in audio processing. However, MFCC features are usually calculated from a single window (taper) characterized by large variance. This study shows investigations on reducing variance for the classification of two different voice qualities (normal voice and disordered voice) using multitaper MFCC features. We also compare their performance by newly proposed windowing techniques and conventional single-taper technique. The results demonstrate that adapted weighted Thomson multitaper method could distinguish between normal voice and disordered voice better than the results done by the conventional single-taper (Hamming window) technique and two newly proposed windowing methods. The multitaper MFCC features may be helpful in identifying voices at risk for a real pathology that has to be proven later.http://dx.doi.org/10.1155/2015/956249
collection DOAJ
language English
format Article
sources DOAJ
author Ömer Eskidere
Ahmet Gürhanlı
spellingShingle Ömer Eskidere
Ahmet Gürhanlı
Voice Disorder Classification Based on Multitaper Mel Frequency Cepstral Coefficients Features
Computational and Mathematical Methods in Medicine
author_facet Ömer Eskidere
Ahmet Gürhanlı
author_sort Ömer Eskidere
title Voice Disorder Classification Based on Multitaper Mel Frequency Cepstral Coefficients Features
title_short Voice Disorder Classification Based on Multitaper Mel Frequency Cepstral Coefficients Features
title_full Voice Disorder Classification Based on Multitaper Mel Frequency Cepstral Coefficients Features
title_fullStr Voice Disorder Classification Based on Multitaper Mel Frequency Cepstral Coefficients Features
title_full_unstemmed Voice Disorder Classification Based on Multitaper Mel Frequency Cepstral Coefficients Features
title_sort voice disorder classification based on multitaper mel frequency cepstral coefficients features
publisher Hindawi Limited
series Computational and Mathematical Methods in Medicine
issn 1748-670X
1748-6718
publishDate 2015-01-01
description The Mel Frequency Cepstral Coefficients (MFCCs) are widely used in order to extract essential information from a voice signal and became a popular feature extractor used in audio processing. However, MFCC features are usually calculated from a single window (taper) characterized by large variance. This study shows investigations on reducing variance for the classification of two different voice qualities (normal voice and disordered voice) using multitaper MFCC features. We also compare their performance by newly proposed windowing techniques and conventional single-taper technique. The results demonstrate that adapted weighted Thomson multitaper method could distinguish between normal voice and disordered voice better than the results done by the conventional single-taper (Hamming window) technique and two newly proposed windowing methods. The multitaper MFCC features may be helpful in identifying voices at risk for a real pathology that has to be proven later.
url http://dx.doi.org/10.1155/2015/956249
work_keys_str_mv AT omereskidere voicedisorderclassificationbasedonmultitapermelfrequencycepstralcoefficientsfeatures
AT ahmetgurhanlı voicedisorderclassificationbasedonmultitapermelfrequencycepstralcoefficientsfeatures
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