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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/87159
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T10:31:54Zoai: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:34982025-09-29 10:31:54.7CONICET 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 |
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reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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13.070432 |