Invariant Measures Based on the U-Correlation Integral: An Application to the Study of Human Voice

Autores
Restrepo Rinckoar, Juan Felipe; Schlotthauer, Gaston
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Nonlinear measures such as the correlation dimension, the correlation entropy, and the noise level were used in this article to characterize normal and pathological voices. These invariants were estimated through an automated algorithm based on the recently proposed U-correlation integral. Our results show that the voice dynamics have a low dimension. The value of correlation dimension is greater for pathological voices than for normal ones. Furthermore, its value also increases along with the type of the voice. The low correlation entropy values obtained for normal and pathological type 1 and type 2 voices suggest that their dynamics are nearly periodic. Regarding the noise level, in the context of voice signals, it can be interpreted as the power of an additive stochastic perturbation intrinsic to the voice production system. Our estimations suggest that the noise level is greater for pathological voices than for normal ones. Moreover, it increases along with the type of voice, being the highest for type voices. From these results, we can conclude that the voice production dynamical system is more complex in the presence of a pathology. In addition, the presence of the inherent stochastic perturbation strengthens along with the voice type. Finally, based on our results, we propose that the noise level can be used to quantitatively differentiate between type and type voices.
Fil: Restrepo Rinckoar, Juan Felipe. Instituto de Investigación y Desarrollo En Bioingeniería y Bioinformática; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Schlotthauer, Gaston. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Investigación y Desarrollo En Bioingeniería y Bioinformática; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; Argentina
Materia
Correlation integral
Correlation entropy
Correlation dimension
Pathological voices
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:11336/96538

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spelling Invariant Measures Based on the U-Correlation Integral: An Application to the Study of Human VoiceRestrepo Rinckoar, Juan FelipeSchlotthauer, GastonCorrelation integralCorrelation entropyCorrelation dimensionPathological voiceshttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Nonlinear measures such as the correlation dimension, the correlation entropy, and the noise level were used in this article to characterize normal and pathological voices. These invariants were estimated through an automated algorithm based on the recently proposed U-correlation integral. Our results show that the voice dynamics have a low dimension. The value of correlation dimension is greater for pathological voices than for normal ones. Furthermore, its value also increases along with the type of the voice. The low correlation entropy values obtained for normal and pathological type 1 and type 2 voices suggest that their dynamics are nearly periodic. Regarding the noise level, in the context of voice signals, it can be interpreted as the power of an additive stochastic perturbation intrinsic to the voice production system. Our estimations suggest that the noise level is greater for pathological voices than for normal ones. Moreover, it increases along with the type of voice, being the highest for type voices. From these results, we can conclude that the voice production dynamical system is more complex in the presence of a pathology. In addition, the presence of the inherent stochastic perturbation strengthens along with the voice type. Finally, based on our results, we propose that the noise level can be used to quantitatively differentiate between type and type voices.Fil: Restrepo Rinckoar, Juan Felipe. Instituto de Investigación y Desarrollo En Bioingeniería y Bioinformática; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Schlotthauer, Gaston. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Investigación y Desarrollo En Bioingeniería y Bioinformática; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; ArgentinaJohn Wiley & Sons Inc2018-04info: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/11336/96538Restrepo Rinckoar, Juan Felipe; Schlotthauer, Gaston; Invariant Measures Based on the U-Correlation Integral: An Application to the Study of Human Voice; John Wiley & Sons Inc; Complexity; 2018; 4-2018; 1-91076-2787CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.hindawi.com/journals/complexity/2018/2173640/info:eu-repo/semantics/altIdentifier/doi/10.1155/2018/2173640info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:37:37Zoai:ri.conicet.gov.ar:11336/96538instacron: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 15:37:38.16CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Invariant Measures Based on the U-Correlation Integral: An Application to the Study of Human Voice
title Invariant Measures Based on the U-Correlation Integral: An Application to the Study of Human Voice
spellingShingle Invariant Measures Based on the U-Correlation Integral: An Application to the Study of Human Voice
Restrepo Rinckoar, Juan Felipe
Correlation integral
Correlation entropy
Correlation dimension
Pathological voices
title_short Invariant Measures Based on the U-Correlation Integral: An Application to the Study of Human Voice
title_full Invariant Measures Based on the U-Correlation Integral: An Application to the Study of Human Voice
title_fullStr Invariant Measures Based on the U-Correlation Integral: An Application to the Study of Human Voice
title_full_unstemmed Invariant Measures Based on the U-Correlation Integral: An Application to the Study of Human Voice
title_sort Invariant Measures Based on the U-Correlation Integral: An Application to the Study of Human Voice
dc.creator.none.fl_str_mv Restrepo Rinckoar, Juan Felipe
Schlotthauer, Gaston
author Restrepo Rinckoar, Juan Felipe
author_facet Restrepo Rinckoar, Juan Felipe
Schlotthauer, Gaston
author_role author
author2 Schlotthauer, Gaston
author2_role author
dc.subject.none.fl_str_mv Correlation integral
Correlation entropy
Correlation dimension
Pathological voices
topic Correlation integral
Correlation entropy
Correlation dimension
Pathological voices
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Nonlinear measures such as the correlation dimension, the correlation entropy, and the noise level were used in this article to characterize normal and pathological voices. These invariants were estimated through an automated algorithm based on the recently proposed U-correlation integral. Our results show that the voice dynamics have a low dimension. The value of correlation dimension is greater for pathological voices than for normal ones. Furthermore, its value also increases along with the type of the voice. The low correlation entropy values obtained for normal and pathological type 1 and type 2 voices suggest that their dynamics are nearly periodic. Regarding the noise level, in the context of voice signals, it can be interpreted as the power of an additive stochastic perturbation intrinsic to the voice production system. Our estimations suggest that the noise level is greater for pathological voices than for normal ones. Moreover, it increases along with the type of voice, being the highest for type voices. From these results, we can conclude that the voice production dynamical system is more complex in the presence of a pathology. In addition, the presence of the inherent stochastic perturbation strengthens along with the voice type. Finally, based on our results, we propose that the noise level can be used to quantitatively differentiate between type and type voices.
Fil: Restrepo Rinckoar, Juan Felipe. Instituto de Investigación y Desarrollo En Bioingeniería y Bioinformática; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Schlotthauer, Gaston. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Investigación y Desarrollo En Bioingeniería y Bioinformática; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; Argentina
description Nonlinear measures such as the correlation dimension, the correlation entropy, and the noise level were used in this article to characterize normal and pathological voices. These invariants were estimated through an automated algorithm based on the recently proposed U-correlation integral. Our results show that the voice dynamics have a low dimension. The value of correlation dimension is greater for pathological voices than for normal ones. Furthermore, its value also increases along with the type of the voice. The low correlation entropy values obtained for normal and pathological type 1 and type 2 voices suggest that their dynamics are nearly periodic. Regarding the noise level, in the context of voice signals, it can be interpreted as the power of an additive stochastic perturbation intrinsic to the voice production system. Our estimations suggest that the noise level is greater for pathological voices than for normal ones. Moreover, it increases along with the type of voice, being the highest for type voices. From these results, we can conclude that the voice production dynamical system is more complex in the presence of a pathology. In addition, the presence of the inherent stochastic perturbation strengthens along with the voice type. Finally, based on our results, we propose that the noise level can be used to quantitatively differentiate between type and type voices.
publishDate 2018
dc.date.none.fl_str_mv 2018-04
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/11336/96538
Restrepo Rinckoar, Juan Felipe; Schlotthauer, Gaston; Invariant Measures Based on the U-Correlation Integral: An Application to the Study of Human Voice; John Wiley & Sons Inc; Complexity; 2018; 4-2018; 1-9
1076-2787
CONICET Digital
CONICET
url http://hdl.handle.net/11336/96538
identifier_str_mv Restrepo Rinckoar, Juan Felipe; Schlotthauer, Gaston; Invariant Measures Based on the U-Correlation Integral: An Application to the Study of Human Voice; John Wiley & Sons Inc; Complexity; 2018; 4-2018; 1-9
1076-2787
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.hindawi.com/journals/complexity/2018/2173640/
info:eu-repo/semantics/altIdentifier/doi/10.1155/2018/2173640
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
dc.publisher.none.fl_str_mv John Wiley & Sons Inc
publisher.none.fl_str_mv John Wiley & Sons Inc
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