Using multiple frequency bins for stabilization of FD-ICA algorithms

Autores
Di Persia, Leandro Ezequiel; Milone, Diego Humberto
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In the frequency domain independent component analysis approaches for audiosources separation, the convolutive mixing problem is replaced by thesolution of several instantaneous mixing problems, one for each frequencybin of the short time Fourier transform. This methodology yields good resultsbut requires the solution of the permutation ambiguity. Moreover, theperformance of the separation algorithms for each bin is not guaranteed tobe equivalent, thus some bins can have worse results than others. In thispaper a technique based on data from multiple bins is proposed to addressthese issues. The use of multiple bin information produces a coupling of theseparation, resulting in more stable separation matrices and reducing the occurrence of permutations, but increasing in computational cost. This can bemitigated by a sub sampling of the multiple bins information. The resultsshow that both approaches are beneficial for the frequency domain ICA approach,producing better separation in terms of objective quality measures.
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: Milone, Diego Humberto. 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
Materia
Audio Sources Separation
Frequency Domain Independent Component Analysis
Multiple Bins
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/47800
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Using multiple frequency bins for stabilization of FD-ICA algorithmsDi Persia, Leandro EzequielMilone, Diego HumbertoAudio Sources SeparationFrequency Domain Independent Component AnalysisMultiple Binshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1In the frequency domain independent component analysis approaches for audiosources separation, the convolutive mixing problem is replaced by thesolution of several instantaneous mixing problems, one for each frequencybin of the short time Fourier transform. This methodology yields good resultsbut requires the solution of the permutation ambiguity. Moreover, theperformance of the separation algorithms for each bin is not guaranteed tobe equivalent, thus some bins can have worse results than others. In thispaper a technique based on data from multiple bins is proposed to addressthese issues. The use of multiple bin information produces a coupling of theseparation, resulting in more stable separation matrices and reducing the occurrence of permutations, but increasing in computational cost. This can bemitigated by a sub sampling of the multiple bins information. The resultsshow that both approaches are beneficial for the frequency domain ICA approach,producing better separation in terms of objective quality measures.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; ArgentinaFil: Milone, Diego Humberto. 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; ArgentinaElsevier Science2016-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/47800Di Persia, Leandro Ezequiel; Milone, Diego Humberto; Using multiple frequency bins for stabilization of FD-ICA algorithms; Elsevier Science; Signal Processing; 119; 2-2016; 162-1680165-1684CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.sigpro.2015.07.025info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0165168415002625info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:27:54Zoai:ri.conicet.gov.ar:11336/47800instacron: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 15:27:54.819CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Using multiple frequency bins for stabilization of FD-ICA algorithms
title Using multiple frequency bins for stabilization of FD-ICA algorithms
spellingShingle Using multiple frequency bins for stabilization of FD-ICA algorithms
Di Persia, Leandro Ezequiel
Audio Sources Separation
Frequency Domain Independent Component Analysis
Multiple Bins
title_short Using multiple frequency bins for stabilization of FD-ICA algorithms
title_full Using multiple frequency bins for stabilization of FD-ICA algorithms
title_fullStr Using multiple frequency bins for stabilization of FD-ICA algorithms
title_full_unstemmed Using multiple frequency bins for stabilization of FD-ICA algorithms
title_sort Using multiple frequency bins for stabilization of FD-ICA algorithms
dc.creator.none.fl_str_mv Di Persia, Leandro Ezequiel
Milone, Diego Humberto
author Di Persia, Leandro Ezequiel
author_facet Di Persia, Leandro Ezequiel
Milone, Diego Humberto
author_role author
author2 Milone, Diego Humberto
author2_role author
dc.subject.none.fl_str_mv Audio Sources Separation
Frequency Domain Independent Component Analysis
Multiple Bins
topic Audio Sources Separation
Frequency Domain Independent Component Analysis
Multiple Bins
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 the frequency domain independent component analysis approaches for audiosources separation, the convolutive mixing problem is replaced by thesolution of several instantaneous mixing problems, one for each frequencybin of the short time Fourier transform. This methodology yields good resultsbut requires the solution of the permutation ambiguity. Moreover, theperformance of the separation algorithms for each bin is not guaranteed tobe equivalent, thus some bins can have worse results than others. In thispaper a technique based on data from multiple bins is proposed to addressthese issues. The use of multiple bin information produces a coupling of theseparation, resulting in more stable separation matrices and reducing the occurrence of permutations, but increasing in computational cost. This can bemitigated by a sub sampling of the multiple bins information. The resultsshow that both approaches are beneficial for the frequency domain ICA approach,producing better separation in terms of objective quality measures.
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: Milone, Diego Humberto. 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
description In the frequency domain independent component analysis approaches for audiosources separation, the convolutive mixing problem is replaced by thesolution of several instantaneous mixing problems, one for each frequencybin of the short time Fourier transform. This methodology yields good resultsbut requires the solution of the permutation ambiguity. Moreover, theperformance of the separation algorithms for each bin is not guaranteed tobe equivalent, thus some bins can have worse results than others. In thispaper a technique based on data from multiple bins is proposed to addressthese issues. The use of multiple bin information produces a coupling of theseparation, resulting in more stable separation matrices and reducing the occurrence of permutations, but increasing in computational cost. This can bemitigated by a sub sampling of the multiple bins information. The resultsshow that both approaches are beneficial for the frequency domain ICA approach,producing better separation in terms of objective quality measures.
publishDate 2016
dc.date.none.fl_str_mv 2016-02
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/11336/47800
Di Persia, Leandro Ezequiel; Milone, Diego Humberto; Using multiple frequency bins for stabilization of FD-ICA algorithms; Elsevier Science; Signal Processing; 119; 2-2016; 162-168
0165-1684
CONICET Digital
CONICET
url http://hdl.handle.net/11336/47800
identifier_str_mv Di Persia, Leandro Ezequiel; Milone, Diego Humberto; Using multiple frequency bins for stabilization of FD-ICA algorithms; Elsevier Science; Signal Processing; 119; 2-2016; 162-168
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/doi/10.1016/j.sigpro.2015.07.025
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0165168415002625
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv 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
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score 13.22299

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