Nonlinear analysis and classification of vocal disorders.
Annu Int Conf IEEE Eng Med Biol Soc
; 2007: 6200-3, 2007.
Article
en En
| MEDLINE
| ID: mdl-18003437
This paper suggests a way to investigate pathological voice signals from nonlinear time series analysis for clinical applications. Primarily, self similar characteristics of vocal signals have been obtained by means of a discrete wavelet analysis. Moreover, the approximate entropy of the signals has been calculated as tools for classification. Furthermore, fuzzy c-means clustering has been employed for voice signal classification. Fuzzy membership function has been proposed as a way of quantifying the amount of disorder. The results show that proposed feature vector and classification method are reliable for voice signal analysis and disorder measurement.
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Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Espectrografía del Sonido
/
Medición de la Producción del Habla
/
Algoritmos
/
Reconocimiento de Normas Patrones Automatizadas
/
Trastornos de la Voz
/
Diagnóstico por Computador
/
Lógica Difusa
Tipo de estudio:
Diagnostic_studies
/
Evaluation_studies
Límite:
Humans
Idioma:
En
Revista:
Annu Int Conf IEEE Eng Med Biol Soc
Año:
2007
Tipo del documento:
Article
País de afiliación:
Irán
Pais de publicación:
Estados Unidos