Mostrar el registro sencillo del ítem
dc.contributor.author
Goddard, J.
dc.contributor.author
Schlotthauer, Gaston
dc.contributor.author
Torres, Maria Eugenia
dc.contributor.author
Rufiner, Hugo Leonardo
dc.date.available
2020-02-18T19:11:00Z
dc.date.issued
2009-07
dc.identifier.citation
Goddard, J.; Schlotthauer, Gaston; Torres, Maria Eugenia; Rufiner, Hugo Leonardo; Dimensionality reduction for visualization of normal and pathological speech data; Elsevier; Biomedical Signal Processing and Control; 4; 3; 7-2009; 194-201
dc.identifier.issn
1746-8094
dc.identifier.uri
http://hdl.handle.net/11336/97938
dc.description.abstract
For an adequate analysis of pathological speech signals, a sizeable number of parameters is required, such as those related to jitter, shimmer and noise content. Often this kind of high-dimensional signal representation is difficult to understand, even for expert voice therapists and physicians. Data visualization of a high-dimensional dataset can provide a useful first step in its exploratory data analysis, facilitating an understanding about its underlying structure. In the present paper, eight dimensionality reduction techniques, both classical and recent, are compared on speech data containing normal and pathological speech. A qualitative analysis of their dimensionality reduction capabilities is presented. The transformed data are also quantitatively evaluated, using classifiers, and it is found that it may be advantageous to perform the classification process on the transformed data, rather than on the original. These qualitative and quantitative analyses allow us to conclude that a nonlinear, supervised method, called kernel local Fisher discriminant analysis is superior for dimensionality reduction in the actual context.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
DATA VISUALIZATION
dc.subject
DIMENSIONALITY REDUCTION
dc.subject
KERNEL METHODS
dc.subject
PATHOLOGICAL VOICE ANALYSIS
dc.subject.classification
Otras Ingenierías y Tecnologías
dc.subject.classification
Otras Ingenierías y Tecnologías
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
dc.title
Dimensionality reduction for visualization of normal and pathological speech data
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2020-02-13T18:55:16Z
dc.journal.volume
4
dc.journal.number
3
dc.journal.pagination
194-201
dc.journal.pais
Países Bajos
dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Goddard, J.. Universidad Autónoma Metropolitana; México
dc.description.fil
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; Argentina
dc.description.fil
Fil: Torres, Maria Eugenia. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina
dc.description.fil
Fil: Rufiner, Hugo Leonardo. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones en Señales e Inteligencia Computacional; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.journal.title
Biomedical Signal Processing and Control
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S1746809409000020
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.bspc.2009.01.001
Archivos asociados