Correlated Postfiltering and Mutual Information in Pseudoanechoic Model Based Blind Source Separation

Autores
Di Persia, Leandro Ezequiel; Milone, Diego Humberto; Yanagida, Masuzo
Año de publicación
2011
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In a recent publication the pseudoanechoic mixing model for closely spaced microphones was proposed and a blind audio sources separation algorithm based on this model was developed. This method uses frequency-domain independent component analysis to identify the mixing parameters. These parameters are used to synthesize the separation matrices, and then a time-frequency Wiener postfilter to improve the separation is applied. In this contribution, key aspects of the separation algorithm are optimized with two novel methods. A deeper analysis of the working principles of the Wiener postfilter is presented, which gives an insight in its reverberation reduction capabilities. Also a variation of this postfilter to improve the performance using the information of previous frames is introduced. The basic method uses a fixed central frequency bin for the estimation of the mixture parameters. In this contribution an automatic selection of the central bin, based in the information of the separability of the sources, is introduced. The improvements obtained through these methods are evaluated in an automatic speech recognition task and with the PESQ objective quality measure. The results show an increased robustness and stability of the proposed method, enhancing the separation quality and improving the speech recognition rate of an automatic speech recognition system.
Fil: Di Persia, Leandro Ezequiel. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones En Señales E Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Milone, Diego Humberto. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones En Señales E Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Yanagida, Masuzo. Doshisha University. Department of Intelligent Information Engineering and Science; Estados Unidos
Materia
Pseudoanechoic Model
Blind Source Separation
Mutual Information
Wiener Postfilter
Automatic Speech Recognition
Wiener Postfilter
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:11336/14395
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/14395
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Correlated Postfiltering and Mutual Information in Pseudoanechoic Model Based Blind Source SeparationDi Persia, Leandro EzequielMilone, Diego HumbertoYanagida, MasuzoPseudoanechoic ModelBlind Source SeparationMutual InformationWiener PostfilterAutomatic Speech RecognitionWiener Postfilterhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1In a recent publication the pseudoanechoic mixing model for closely spaced microphones was proposed and a blind audio sources separation algorithm based on this model was developed. This method uses frequency-domain independent component analysis to identify the mixing parameters. These parameters are used to synthesize the separation matrices, and then a time-frequency Wiener postfilter to improve the separation is applied. In this contribution, key aspects of the separation algorithm are optimized with two novel methods. A deeper analysis of the working principles of the Wiener postfilter is presented, which gives an insight in its reverberation reduction capabilities. Also a variation of this postfilter to improve the performance using the information of previous frames is introduced. The basic method uses a fixed central frequency bin for the estimation of the mixture parameters. In this contribution an automatic selection of the central bin, based in the information of the separability of the sources, is introduced. The improvements obtained through these methods are evaluated in an automatic speech recognition task and with the PESQ objective quality measure. The results show an increased robustness and stability of the proposed method, enhancing the separation quality and improving the speech recognition rate of an automatic speech recognition system.Fil: Di Persia, Leandro Ezequiel. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones En Señales E Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Milone, Diego Humberto. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones En Señales E Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Yanagida, Masuzo. Doshisha University. Department of Intelligent Information Engineering and Science; Estados UnidosSpringer2011-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/14395Di Persia, Leandro Ezequiel; Milone, Diego Humberto; Yanagida, Masuzo ; Correlated Postfiltering and Mutual Information in Pseudoanechoic Model Based Blind Source Separation; Springer; Journal Of Signal Processing Systems For Signal Image And Video Technology; 63; 3; 6-2011; 333-3441939-80181939-8115enginfo:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/s11265-009-0443-3info:eu-repo/semantics/altIdentifier/doi//10.1007/s11265-009-0443-3info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:51:17Zoai:ri.conicet.gov.ar:11336/14395instacron: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 09:51:17.775CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Correlated Postfiltering and Mutual Information in Pseudoanechoic Model Based Blind Source Separation
title Correlated Postfiltering and Mutual Information in Pseudoanechoic Model Based Blind Source Separation
spellingShingle Correlated Postfiltering and Mutual Information in Pseudoanechoic Model Based Blind Source Separation
Di Persia, Leandro Ezequiel
Pseudoanechoic Model
Blind Source Separation
Mutual Information
Wiener Postfilter
Automatic Speech Recognition
Wiener Postfilter
title_short Correlated Postfiltering and Mutual Information in Pseudoanechoic Model Based Blind Source Separation
title_full Correlated Postfiltering and Mutual Information in Pseudoanechoic Model Based Blind Source Separation
title_fullStr Correlated Postfiltering and Mutual Information in Pseudoanechoic Model Based Blind Source Separation
title_full_unstemmed Correlated Postfiltering and Mutual Information in Pseudoanechoic Model Based Blind Source Separation
title_sort Correlated Postfiltering and Mutual Information in Pseudoanechoic Model Based Blind Source Separation
dc.creator.none.fl_str_mv Di Persia, Leandro Ezequiel
Milone, Diego Humberto
Yanagida, Masuzo
author Di Persia, Leandro Ezequiel
author_facet Di Persia, Leandro Ezequiel
Milone, Diego Humberto
Yanagida, Masuzo
author_role author
author2 Milone, Diego Humberto
Yanagida, Masuzo
author2_role author
author
dc.subject.none.fl_str_mv Pseudoanechoic Model
Blind Source Separation
Mutual Information
Wiener Postfilter
Automatic Speech Recognition
Wiener Postfilter
topic Pseudoanechoic Model
Blind Source Separation
Mutual Information
Wiener Postfilter
Automatic Speech Recognition
Wiener Postfilter
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In a recent publication the pseudoanechoic mixing model for closely spaced microphones was proposed and a blind audio sources separation algorithm based on this model was developed. This method uses frequency-domain independent component analysis to identify the mixing parameters. These parameters are used to synthesize the separation matrices, and then a time-frequency Wiener postfilter to improve the separation is applied. In this contribution, key aspects of the separation algorithm are optimized with two novel methods. A deeper analysis of the working principles of the Wiener postfilter is presented, which gives an insight in its reverberation reduction capabilities. Also a variation of this postfilter to improve the performance using the information of previous frames is introduced. The basic method uses a fixed central frequency bin for the estimation of the mixture parameters. In this contribution an automatic selection of the central bin, based in the information of the separability of the sources, is introduced. The improvements obtained through these methods are evaluated in an automatic speech recognition task and with the PESQ objective quality measure. The results show an increased robustness and stability of the proposed method, enhancing the separation quality and improving the speech recognition rate of an automatic speech recognition system.
Fil: Di Persia, Leandro Ezequiel. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones En Señales E Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Milone, Diego Humberto. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones En Señales E Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Yanagida, Masuzo. Doshisha University. Department of Intelligent Information Engineering and Science; Estados Unidos
description In a recent publication the pseudoanechoic mixing model for closely spaced microphones was proposed and a blind audio sources separation algorithm based on this model was developed. This method uses frequency-domain independent component analysis to identify the mixing parameters. These parameters are used to synthesize the separation matrices, and then a time-frequency Wiener postfilter to improve the separation is applied. In this contribution, key aspects of the separation algorithm are optimized with two novel methods. A deeper analysis of the working principles of the Wiener postfilter is presented, which gives an insight in its reverberation reduction capabilities. Also a variation of this postfilter to improve the performance using the information of previous frames is introduced. The basic method uses a fixed central frequency bin for the estimation of the mixture parameters. In this contribution an automatic selection of the central bin, based in the information of the separability of the sources, is introduced. The improvements obtained through these methods are evaluated in an automatic speech recognition task and with the PESQ objective quality measure. The results show an increased robustness and stability of the proposed method, enhancing the separation quality and improving the speech recognition rate of an automatic speech recognition system.
publishDate 2011
dc.date.none.fl_str_mv 2011-06
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/11336/14395
Di Persia, Leandro Ezequiel; Milone, Diego Humberto; Yanagida, Masuzo ; Correlated Postfiltering and Mutual Information in Pseudoanechoic Model Based Blind Source Separation; Springer; Journal Of Signal Processing Systems For Signal Image And Video Technology; 63; 3; 6-2011; 333-344
1939-8018
1939-8115
url http://hdl.handle.net/11336/14395
identifier_str_mv Di Persia, Leandro Ezequiel; Milone, Diego Humberto; Yanagida, Masuzo ; Correlated Postfiltering and Mutual Information in Pseudoanechoic Model Based Blind Source Separation; Springer; Journal Of Signal Processing Systems For Signal Image And Video Technology; 63; 3; 6-2011; 333-344
1939-8018
1939-8115
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/s11265-009-0443-3
info:eu-repo/semantics/altIdentifier/doi//10.1007/s11265-009-0443-3
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
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
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_ 1844613577735929856
score 13.070432

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