Identification of vocal fold pathology based on Mel Frequency Band Energy Coefficients and singular value decomposition
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Institute of Electrical and Elctronics Engineering (IEEE)
2010
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my.unimap-86832010-08-16T02:58:27Z Identification of vocal fold pathology based on Mel Frequency Band Energy Coefficients and singular value decomposition Hariharan, Muthusamy Paulraj, Murugesa Pandiyan, Assoc. Prof. Sazali, Yaacob, Prof. Dr. K-nearest neighbour classifier (k-NN) Linear discriminant analysis Mel Frequency Band Energy Coefficients Singular value decomposition Vocal fold pathology International Conference on Signal and Image Processing Applications (ICSIPA) Link to publisher's homepage at http://ieeexplore.ieee.org/ Many approaches have been developed to detect the vocal fold pathology. Among the approaches, analysis of speech has proved to be an excellent tool for vocal fold pathology detection. This paper presents the Mel Frequency Band Energy Coefficients (MFBECs) combined with singular value decomposition (SVD) based feature extraction method for the classification of pathological or normal voice. In order to extract the most relevant information from the original MFBECs feature dataset, SVD is used. For the analysis, the speech samples of pathological and healthy subjects from the Massachusetts Eye and Ear Infirmary (MEEI) database are used. A simple k-means nearest neighbourhood (k-NN) and Linear Discriminant Analysis (LDA) based classifiers are used for testing the effectiveness of the MFBECs-SVD based feature vector. The experimental results show that the proposed features gives very promising classification accuracy and also can be effectively used to detect the pathological voices clinically. 2010-08-16T02:58:27Z 2010-08-16T02:58:27Z 2009-11-18 Working Paper p.514-517 978-1-4244-5561-4 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5478710&tag=1 http://hdl.handle.net/123456789/8683 en Proceedings of the International Conference on Signal and Image Processing Applications (ICSIPA) 2009 Institute of Electrical and Elctronics Engineering (IEEE) |
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K-nearest neighbour classifier (k-NN) Linear discriminant analysis Mel Frequency Band Energy Coefficients Singular value decomposition Vocal fold pathology International Conference on Signal and Image Processing Applications (ICSIPA) |
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K-nearest neighbour classifier (k-NN) Linear discriminant analysis Mel Frequency Band Energy Coefficients Singular value decomposition Vocal fold pathology International Conference on Signal and Image Processing Applications (ICSIPA) Hariharan, Muthusamy Paulraj, Murugesa Pandiyan, Assoc. Prof. Sazali, Yaacob, Prof. Dr. Identification of vocal fold pathology based on Mel Frequency Band Energy Coefficients and singular value decomposition |
| description |
Link to publisher's homepage at http://ieeexplore.ieee.org/ |
| format |
Working Paper |
| author |
Hariharan, Muthusamy Paulraj, Murugesa Pandiyan, Assoc. Prof. Sazali, Yaacob, Prof. Dr. |
| author_facet |
Hariharan, Muthusamy Paulraj, Murugesa Pandiyan, Assoc. Prof. Sazali, Yaacob, Prof. Dr. |
| author_sort |
Hariharan, Muthusamy |
| title |
Identification of vocal fold pathology based on Mel Frequency Band Energy Coefficients and singular value decomposition |
| title_short |
Identification of vocal fold pathology based on Mel Frequency Band Energy Coefficients and singular value decomposition |
| title_full |
Identification of vocal fold pathology based on Mel Frequency Band Energy Coefficients and singular value decomposition |
| title_fullStr |
Identification of vocal fold pathology based on Mel Frequency Band Energy Coefficients and singular value decomposition |
| title_full_unstemmed |
Identification of vocal fold pathology based on Mel Frequency Band Energy Coefficients and singular value decomposition |
| title_sort |
identification of vocal fold pathology based on mel frequency band energy coefficients and singular value decomposition |
| publisher |
Institute of Electrical and Elctronics Engineering (IEEE) |
| publishDate |
2010 |
| url |
http://dspace.unimap.edu.my/xmlui/handle/123456789/8683 |
| _version_ |
1643789249625980928 |
| score |
13.159267 |