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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Series: | Computational and Mathematical Methods in Medicine |
Online Access: | http://dx.doi.org/10.1155/2015/956249 |
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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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1725099489988968448 |