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dc.contributor.authorMurugesa Pandian, Paulraj, Prof. Madya Dr,
dc.contributor.authorSazali, Yaacob, Prof. Dr.
dc.contributor.authorMuthusamy, Hariharan, Dr.
dc.date.accessioned2011-10-07T06:12:41Z
dc.date.available2011-10-07T06:12:41Z
dc.date.issued2008-06-25
dc.identifier.citationIFMBE Proceedings, vol. 21(1), 2008, pages 790-793en_US
dc.identifier.isbn978-354069138-9
dc.identifier.issn1680-0737
dc.identifier.urihttp://www.springerlink.com/content/j57572n370x4x802/fulltext.pdf
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/14048
dc.descriptionLink to publisher's homepage at http://springerlink.com/en_US
dc.description.abstractFeature extraction from the vocal signal plays very important role in the area of automatic detection of voice disorders. Many feature extraction algorithms have been developed in the last three decades based on acoustic analysis of speech signals. To provide a robust representation of speech signal in an automatic voice disorder diagnosis system, a Melscaled wavelet packet transform has been suggested. A simple neural network model is designed to test the efficacy of Melscaled wavelet packet transform based features. Experimental results show that the features derived by using Melscaled wavelet packet transform provides comparable results. The proposed features can be used as an additional acoustic indicator for the evaluation of voice disorders.en_US
dc.language.isoenen_US
dc.publisherSpringerLinken_US
dc.relation.ispartofseriesProceedings of the 4th Kuala Lumpur International Conference on Biomedical Engineering 2008en_US
dc.subjectFeature extractionen_US
dc.subjectAcoustic analysisen_US
dc.subjectMelscaled wavelet packet transformen_US
dc.subjectNeural networken_US
dc.titleFeature extraction based on mel-scaled wavelet packet transform for the diagnosis of voice disordersen_US
dc.typeWorking Paperen_US
dc.contributor.urlpaul@unimap.edu.myen_US


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