A Bayesian approach to convolutive nonnegative matrix factorization for blind speech dereverberation

Autores
Ibarrola, Francisco Javier; Di Persia, Leandro Ezequiel; Spies, Ruben Daniel
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
When a signal is recorded in an enclosed room, it typically gets affected by reverberation. This degradation represents a problem when dealing with audio signals, particularly in the field of speech signal processing, such as automatic speech recognition. Although there are some approaches to deal with this issue that are quite satisfactory under certain conditions, constructing a method that works well in a general context still poses a significant challenge. In this article, we propose a Bayesian approach based on convolutive nonnegative matrix factorization that uses prior distributions in order to impose certain characteristics over the time-frequency components of the restored signal and the reverberant components. An algorithm for implementing the method is described and tested.Comparisons of the results against those obtained with state-of-the-art methods are presented, showing significant improvement.
Fil: Ibarrola, Francisco Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Di Persia, Leandro Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Spies, Ruben Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina
Materia
Signal processing
Dereverberation
Regularization
Signal processing
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/87159
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A Bayesian approach to convolutive nonnegative matrix factorization for blind speech dereverberationIbarrola, Francisco JavierDi Persia, Leandro EzequielSpies, Ruben DanielSignal processingDereverberationRegularizationSignal processinghttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1When a signal is recorded in an enclosed room, it typically gets affected by reverberation. This degradation represents a problem when dealing with audio signals, particularly in the field of speech signal processing, such as automatic speech recognition. Although there are some approaches to deal with this issue that are quite satisfactory under certain conditions, constructing a method that works well in a general context still poses a significant challenge. In this article, we propose a Bayesian approach based on convolutive nonnegative matrix factorization that uses prior distributions in order to impose certain characteristics over the time-frequency components of the restored signal and the reverberant components. An algorithm for implementing the method is described and tested.Comparisons of the results against those obtained with state-of-the-art methods are presented, showing significant improvement.Fil: Ibarrola, Francisco Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Di Persia, Leandro Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Spies, Ruben Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; ArgentinaElsevier Science2018-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/87159Ibarrola, Francisco Javier; Di Persia, Leandro Ezequiel; Spies, Ruben Daniel; A Bayesian approach to convolutive nonnegative matrix factorization for blind speech dereverberation; Elsevier Science; Signal Processing; 151; 10-2018; 89-980165-1684CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://linkinghub.elsevier.com/retrieve/pii/S0165168418301518info:eu-repo/semantics/altIdentifier/doi/10.1016/j.sigpro.2018.04.024info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:29:15Zoai:ri.conicet.gov.ar:11336/87159instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 15:29:16.123CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A Bayesian approach to convolutive nonnegative matrix factorization for blind speech dereverberation
title A Bayesian approach to convolutive nonnegative matrix factorization for blind speech dereverberation
spellingShingle A Bayesian approach to convolutive nonnegative matrix factorization for blind speech dereverberation
Ibarrola, Francisco Javier
Signal processing
Dereverberation
Regularization
Signal processing
title_short A Bayesian approach to convolutive nonnegative matrix factorization for blind speech dereverberation
title_full A Bayesian approach to convolutive nonnegative matrix factorization for blind speech dereverberation
title_fullStr A Bayesian approach to convolutive nonnegative matrix factorization for blind speech dereverberation
title_full_unstemmed A Bayesian approach to convolutive nonnegative matrix factorization for blind speech dereverberation
title_sort A Bayesian approach to convolutive nonnegative matrix factorization for blind speech dereverberation
dc.creator.none.fl_str_mv Ibarrola, Francisco Javier
Di Persia, Leandro Ezequiel
Spies, Ruben Daniel
author Ibarrola, Francisco Javier
author_facet Ibarrola, Francisco Javier
Di Persia, Leandro Ezequiel
Spies, Ruben Daniel
author_role author
author2 Di Persia, Leandro Ezequiel
Spies, Ruben Daniel
author2_role author
author
dc.subject.none.fl_str_mv Signal processing
Dereverberation
Regularization
Signal processing
topic Signal processing
Dereverberation
Regularization
Signal processing
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv When a signal is recorded in an enclosed room, it typically gets affected by reverberation. This degradation represents a problem when dealing with audio signals, particularly in the field of speech signal processing, such as automatic speech recognition. Although there are some approaches to deal with this issue that are quite satisfactory under certain conditions, constructing a method that works well in a general context still poses a significant challenge. In this article, we propose a Bayesian approach based on convolutive nonnegative matrix factorization that uses prior distributions in order to impose certain characteristics over the time-frequency components of the restored signal and the reverberant components. An algorithm for implementing the method is described and tested.Comparisons of the results against those obtained with state-of-the-art methods are presented, showing significant improvement.
Fil: Ibarrola, Francisco Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Di Persia, Leandro Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Spies, Ruben Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina
description When a signal is recorded in an enclosed room, it typically gets affected by reverberation. This degradation represents a problem when dealing with audio signals, particularly in the field of speech signal processing, such as automatic speech recognition. Although there are some approaches to deal with this issue that are quite satisfactory under certain conditions, constructing a method that works well in a general context still poses a significant challenge. In this article, we propose a Bayesian approach based on convolutive nonnegative matrix factorization that uses prior distributions in order to impose certain characteristics over the time-frequency components of the restored signal and the reverberant components. An algorithm for implementing the method is described and tested.Comparisons of the results against those obtained with state-of-the-art methods are presented, showing significant improvement.
publishDate 2018
dc.date.none.fl_str_mv 2018-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/87159
Ibarrola, Francisco Javier; Di Persia, Leandro Ezequiel; Spies, Ruben Daniel; A Bayesian approach to convolutive nonnegative matrix factorization for blind speech dereverberation; Elsevier Science; Signal Processing; 151; 10-2018; 89-98
0165-1684
CONICET Digital
CONICET
url http://hdl.handle.net/11336/87159
identifier_str_mv Ibarrola, Francisco Javier; Di Persia, Leandro Ezequiel; Spies, Ruben Daniel; A Bayesian approach to convolutive nonnegative matrix factorization for blind speech dereverberation; Elsevier Science; Signal Processing; 151; 10-2018; 89-98
0165-1684
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://linkinghub.elsevier.com/retrieve/pii/S0165168418301518
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.sigpro.2018.04.024
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846083432239595520
score 13.22299

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