Empirical Mode Decomposition for adaptive AM-FM analysis of Speech: A Review

Autores
Sharma, Rajib; Vignolo, Leandro Daniel; Schlotthauer, Gaston; Colominas, Marcelo Alejandro; Rufiner, Hugo Leonardo; Prasanna, S. R. M.
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This work reviews the advancements in the non-conventional analysis of speech signals, particularly from an AM-FM analysis point of view. The benefits of such an analysis, as opposed to the traditional shorttime analysis of speech, is illustrated in this work. The inherent non-linearity of the speech productionsystem is discussed. The limitations of Fourier analysis, Linear Prediction (LP) analysis, and the Mel Filterbank Cepstral Coefficients (MFCCs), are presented, thus providing the motivation for the AM-FM representation of speech. The principle and methodology of traditional AM-FM analysis is discussed, as amethod of capturing the non-linear dynamics of the speech signal. The technique of Empirical Mode Decomposition (EMD) is then introduced as a means of performing adaptive AM-FM analysis of speech, alleviating the limitations of the fixed analysis provided by the traditional AM-FM methodology. The merits and demerits of EMD with respect to traditional AM-FM analysis is discussed. The developments of EMD to counter its demerits are presented. Selected applications of EMD in speech processing are briefly reviewed. The paper concludes by pointing out some aspects of speech processing where EMD might be explored.
Fil: Sharma, Rajib. Indian Institute Of Technology Guwahati; India
Fil: Vignolo, Leandro Daniel. 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: Schlotthauer, Gaston. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Colominas, Marcelo Alejandro. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Rufiner, Hugo Leonardo. 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: Prasanna, S. R. M.. Indian Institute Of Technology Guwahati; India
Materia
Emd
Am-Fm
Wavelet
Lp
Mfcc
Speech 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:11336/47574
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Empirical Mode Decomposition for adaptive AM-FM analysis of Speech: A ReviewSharma, RajibVignolo, Leandro DanielSchlotthauer, GastonColominas, Marcelo AlejandroRufiner, Hugo LeonardoPrasanna, S. R. M.EmdAm-FmWaveletLpMfccSpeech Processinghttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2This work reviews the advancements in the non-conventional analysis of speech signals, particularly from an AM-FM analysis point of view. The benefits of such an analysis, as opposed to the traditional shorttime analysis of speech, is illustrated in this work. The inherent non-linearity of the speech productionsystem is discussed. The limitations of Fourier analysis, Linear Prediction (LP) analysis, and the Mel Filterbank Cepstral Coefficients (MFCCs), are presented, thus providing the motivation for the AM-FM representation of speech. The principle and methodology of traditional AM-FM analysis is discussed, as amethod of capturing the non-linear dynamics of the speech signal. The technique of Empirical Mode Decomposition (EMD) is then introduced as a means of performing adaptive AM-FM analysis of speech, alleviating the limitations of the fixed analysis provided by the traditional AM-FM methodology. The merits and demerits of EMD with respect to traditional AM-FM analysis is discussed. The developments of EMD to counter its demerits are presented. Selected applications of EMD in speech processing are briefly reviewed. The paper concludes by pointing out some aspects of speech processing where EMD might be explored.Fil: Sharma, Rajib. Indian Institute Of Technology Guwahati; IndiaFil: Vignolo, Leandro Daniel. 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: Schlotthauer, Gaston. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Colominas, Marcelo Alejandro. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Rufiner, Hugo Leonardo. 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: Prasanna, S. R. M.. Indian Institute Of Technology Guwahati; IndiaElsevier Science2017-04info: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/11336/47574Sharma, Rajib; Vignolo, Leandro Daniel; Schlotthauer, Gaston; Colominas, Marcelo Alejandro; Rufiner, Hugo Leonardo; et al.; Empirical Mode Decomposition for adaptive AM-FM analysis of Speech: A Review; Elsevier Science; Speech Communication; 88; 4-2017; 39-640167-6393CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0167639316302370info:eu-repo/semantics/altIdentifier/doi/10.1016/j.specom.2016.12.004info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:36:53Zoai:ri.conicet.gov.ar:11336/47574instacron: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 09:36:54.266CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Empirical Mode Decomposition for adaptive AM-FM analysis of Speech: A Review
title Empirical Mode Decomposition for adaptive AM-FM analysis of Speech: A Review
spellingShingle Empirical Mode Decomposition for adaptive AM-FM analysis of Speech: A Review
Sharma, Rajib
Emd
Am-Fm
Wavelet
Lp
Mfcc
Speech Processing
title_short Empirical Mode Decomposition for adaptive AM-FM analysis of Speech: A Review
title_full Empirical Mode Decomposition for adaptive AM-FM analysis of Speech: A Review
title_fullStr Empirical Mode Decomposition for adaptive AM-FM analysis of Speech: A Review
title_full_unstemmed Empirical Mode Decomposition for adaptive AM-FM analysis of Speech: A Review
title_sort Empirical Mode Decomposition for adaptive AM-FM analysis of Speech: A Review
dc.creator.none.fl_str_mv Sharma, Rajib
Vignolo, Leandro Daniel
Schlotthauer, Gaston
Colominas, Marcelo Alejandro
Rufiner, Hugo Leonardo
Prasanna, S. R. M.
author Sharma, Rajib
author_facet Sharma, Rajib
Vignolo, Leandro Daniel
Schlotthauer, Gaston
Colominas, Marcelo Alejandro
Rufiner, Hugo Leonardo
Prasanna, S. R. M.
author_role author
author2 Vignolo, Leandro Daniel
Schlotthauer, Gaston
Colominas, Marcelo Alejandro
Rufiner, Hugo Leonardo
Prasanna, S. R. M.
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Emd
Am-Fm
Wavelet
Lp
Mfcc
Speech Processing
topic Emd
Am-Fm
Wavelet
Lp
Mfcc
Speech Processing
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv This work reviews the advancements in the non-conventional analysis of speech signals, particularly from an AM-FM analysis point of view. The benefits of such an analysis, as opposed to the traditional shorttime analysis of speech, is illustrated in this work. The inherent non-linearity of the speech productionsystem is discussed. The limitations of Fourier analysis, Linear Prediction (LP) analysis, and the Mel Filterbank Cepstral Coefficients (MFCCs), are presented, thus providing the motivation for the AM-FM representation of speech. The principle and methodology of traditional AM-FM analysis is discussed, as amethod of capturing the non-linear dynamics of the speech signal. The technique of Empirical Mode Decomposition (EMD) is then introduced as a means of performing adaptive AM-FM analysis of speech, alleviating the limitations of the fixed analysis provided by the traditional AM-FM methodology. The merits and demerits of EMD with respect to traditional AM-FM analysis is discussed. The developments of EMD to counter its demerits are presented. Selected applications of EMD in speech processing are briefly reviewed. The paper concludes by pointing out some aspects of speech processing where EMD might be explored.
Fil: Sharma, Rajib. Indian Institute Of Technology Guwahati; India
Fil: Vignolo, Leandro Daniel. 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: Schlotthauer, Gaston. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Colominas, Marcelo Alejandro. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Rufiner, Hugo Leonardo. 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: Prasanna, S. R. M.. Indian Institute Of Technology Guwahati; India
description This work reviews the advancements in the non-conventional analysis of speech signals, particularly from an AM-FM analysis point of view. The benefits of such an analysis, as opposed to the traditional shorttime analysis of speech, is illustrated in this work. The inherent non-linearity of the speech productionsystem is discussed. The limitations of Fourier analysis, Linear Prediction (LP) analysis, and the Mel Filterbank Cepstral Coefficients (MFCCs), are presented, thus providing the motivation for the AM-FM representation of speech. The principle and methodology of traditional AM-FM analysis is discussed, as amethod of capturing the non-linear dynamics of the speech signal. The technique of Empirical Mode Decomposition (EMD) is then introduced as a means of performing adaptive AM-FM analysis of speech, alleviating the limitations of the fixed analysis provided by the traditional AM-FM methodology. The merits and demerits of EMD with respect to traditional AM-FM analysis is discussed. The developments of EMD to counter its demerits are presented. Selected applications of EMD in speech processing are briefly reviewed. The paper concludes by pointing out some aspects of speech processing where EMD might be explored.
publishDate 2017
dc.date.none.fl_str_mv 2017-04
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/11336/47574
Sharma, Rajib; Vignolo, Leandro Daniel; Schlotthauer, Gaston; Colominas, Marcelo Alejandro; Rufiner, Hugo Leonardo; et al.; Empirical Mode Decomposition for adaptive AM-FM analysis of Speech: A Review; Elsevier Science; Speech Communication; 88; 4-2017; 39-64
0167-6393
CONICET Digital
CONICET
url http://hdl.handle.net/11336/47574
identifier_str_mv Sharma, Rajib; Vignolo, Leandro Daniel; Schlotthauer, Gaston; Colominas, Marcelo Alejandro; Rufiner, Hugo Leonardo; et al.; Empirical Mode Decomposition for adaptive AM-FM analysis of Speech: A Review; Elsevier Science; Speech Communication; 88; 4-2017; 39-64
0167-6393
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://www.sciencedirect.com/science/article/pii/S0167639316302370
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.specom.2016.12.004
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
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score 13.070432

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