The use of Wavelet packet transform and artificial neural networks in analysis and classification of dysphonic voices
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Data de Publicação: | 2007 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/27585 |
Resumo: | This paper presents a dysphonic voice classification system using the wavelet packet transform and the best basis algorithm (BBA) as dimensionality reductor and 06 artificial neural networks (ANN) acting as specialist systems. Each ANN was a 03-layer multilayer perceptron with 64 input nodes, 01 output node and in the intermediary layer the number of neurons depends on the related training pathology group. The dysphonic voice database was separated in five pathology groups and one healthy control group. Each ANN was trained and associated with one of the 06 groups, and fed by the best base tree (BBT) nodes’ entropy values, using the multiple cross validation (MCV) method and the leave-one-out (LOO) variation technique and success rates obtained were 87.5%, 95.31%, 87.5%, 100%, 96.87% and 89.06% for the groups 01 to 06, respectively. |
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Crovato, César David ParedesSchuck Junior, Adalberto2011-01-28T05:59:12Z20070018-9294http://hdl.handle.net/10183/27585000608313This paper presents a dysphonic voice classification system using the wavelet packet transform and the best basis algorithm (BBA) as dimensionality reductor and 06 artificial neural networks (ANN) acting as specialist systems. Each ANN was a 03-layer multilayer perceptron with 64 input nodes, 01 output node and in the intermediary layer the number of neurons depends on the related training pathology group. The dysphonic voice database was separated in five pathology groups and one healthy control group. Each ANN was trained and associated with one of the 06 groups, and fed by the best base tree (BBT) nodes’ entropy values, using the multiple cross validation (MCV) method and the leave-one-out (LOO) variation technique and success rates obtained were 87.5%, 95.31%, 87.5%, 100%, 96.87% and 89.06% for the groups 01 to 06, respectively.application/pdfengIEEE transactions on biomedical engineering. New York, NY. vol. 54, no. 10 (oct. 2007), p. 1898-1900.Redes neurais artificiaisProcessamento de sinais de vozTransformadas waveletVozAcoustical analysis of voicesArtificial neural networkDysphonic voice classificationWavelet packet transformThe use of Wavelet packet transform and artificial neural networks in analysis and classification of dysphonic voicesEstrangeiroinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSORIGINAL000608313.pdf000608313.pdfTexto completo (inglês)application/pdf111468http://www.lume.ufrgs.br/bitstream/10183/27585/1/000608313.pdfeef170e252580b3346639e99d8c7f2e6MD51TEXT000608313.pdf.txt000608313.pdf.txtExtracted Texttext/plain20926http://www.lume.ufrgs.br/bitstream/10183/27585/2/000608313.pdf.txtc8e774488e5672bd45943d44910c0c65MD52THUMBNAIL000608313.pdf.jpg000608313.pdf.jpgGenerated Thumbnailimage/jpeg2296http://www.lume.ufrgs.br/bitstream/10183/27585/3/000608313.pdf.jpg30cf0b3f1f1c6b56e4fde6d365609c75MD5310183/275852021-06-13 04:29:09.856272oai:www.lume.ufrgs.br:10183/27585Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2021-06-13T07:29:09Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
The use of Wavelet packet transform and artificial neural networks in analysis and classification of dysphonic voices |
title |
The use of Wavelet packet transform and artificial neural networks in analysis and classification of dysphonic voices |
spellingShingle |
The use of Wavelet packet transform and artificial neural networks in analysis and classification of dysphonic voices Crovato, César David Paredes Redes neurais artificiais Processamento de sinais de voz Transformadas wavelet Voz Acoustical analysis of voices Artificial neural network Dysphonic voice classification Wavelet packet transform |
title_short |
The use of Wavelet packet transform and artificial neural networks in analysis and classification of dysphonic voices |
title_full |
The use of Wavelet packet transform and artificial neural networks in analysis and classification of dysphonic voices |
title_fullStr |
The use of Wavelet packet transform and artificial neural networks in analysis and classification of dysphonic voices |
title_full_unstemmed |
The use of Wavelet packet transform and artificial neural networks in analysis and classification of dysphonic voices |
title_sort |
The use of Wavelet packet transform and artificial neural networks in analysis and classification of dysphonic voices |
author |
Crovato, César David Paredes |
author_facet |
Crovato, César David Paredes Schuck Junior, Adalberto |
author_role |
author |
author2 |
Schuck Junior, Adalberto |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Crovato, César David Paredes Schuck Junior, Adalberto |
dc.subject.por.fl_str_mv |
Redes neurais artificiais Processamento de sinais de voz Transformadas wavelet Voz |
topic |
Redes neurais artificiais Processamento de sinais de voz Transformadas wavelet Voz Acoustical analysis of voices Artificial neural network Dysphonic voice classification Wavelet packet transform |
dc.subject.eng.fl_str_mv |
Acoustical analysis of voices Artificial neural network Dysphonic voice classification Wavelet packet transform |
description |
This paper presents a dysphonic voice classification system using the wavelet packet transform and the best basis algorithm (BBA) as dimensionality reductor and 06 artificial neural networks (ANN) acting as specialist systems. Each ANN was a 03-layer multilayer perceptron with 64 input nodes, 01 output node and in the intermediary layer the number of neurons depends on the related training pathology group. The dysphonic voice database was separated in five pathology groups and one healthy control group. Each ANN was trained and associated with one of the 06 groups, and fed by the best base tree (BBT) nodes’ entropy values, using the multiple cross validation (MCV) method and the leave-one-out (LOO) variation technique and success rates obtained were 87.5%, 95.31%, 87.5%, 100%, 96.87% and 89.06% for the groups 01 to 06, respectively. |
publishDate |
2007 |
dc.date.issued.fl_str_mv |
2007 |
dc.date.accessioned.fl_str_mv |
2011-01-28T05:59:12Z |
dc.type.driver.fl_str_mv |
Estrangeiro info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10183/27585 |
dc.identifier.issn.pt_BR.fl_str_mv |
0018-9294 |
dc.identifier.nrb.pt_BR.fl_str_mv |
000608313 |
identifier_str_mv |
0018-9294 000608313 |
url |
http://hdl.handle.net/10183/27585 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
IEEE transactions on biomedical engineering. New York, NY. vol. 54, no. 10 (oct. 2007), p. 1898-1900. |
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info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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application/pdf |
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