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DOI: https://doi.org/10.15332/iteckne.v16i2.2356

Clasificación de género basada en señales de voz mediante modelos difusos y algoritmos de optimización

Gender classification based on voice signals using fuzzy models and optimization algorithms

Luis Miguel Cortés-Martinez, Helbert Eduardo Espitia-Cuchango

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Abstract(es_ES)

En este documento se describe un esquema de clasificación de género, basado en señales de voz, en el que se proponen y prueban 16 modelos difusos diferentes que son optimizados mediante cuatro algoritmos bioinspirados y el método cuasi-Newton. El esquema de clasificación considera cuatro conjuntos de datos y cinco características de voz diferentes para definir los valores de entrada de un algoritmo en el proceso de optimización. Los valores de entrada de cada modelo difuso definen la media y varianza de sus funciones de pertenencia gaussianas, y su desempeño se evalúa mediante los valores de entrada del algoritmo de optimización y el error cuadrático medio como función objetivo para minimizar. Se hace un análisis comparativo entre modelos, algoritmos y conjuntos de datos para obtener conclusiones de acuerdo con los resultados de cada modelo optimizado.

Keywords(es_ES)

Lógica difusa; optimización; algoritmos genéticos; búsqueda armónica; evolución diferencial; optimización con enjambre de partículas; método cuasi-Newton; clasificación de género

Abstract(en_US)

This paper describes a gender classification scheme based on voice signals in which 16 different fuzzy models are proposed and optimized using four bio-inspired optimization algorithms and the quasi-Newton method. The classification scheme considers four data sets and five different voice features to define the input values of an algorithm in the optimization process. The inputs of each fuzzy model define the mean and variance of their Gaussian membership functions, and their fitness is evaluated by the input values of the algorithm and mean squared error as objective function to be minimized. A comparative analysis between models, algorithms and data sets is made to obtain conclusions according to the results of each optimized model.

Keywords(en_US)

Fuzzy logic; optimization; genetic algorithms; harmony search; differential evolution; particle swarm optimization; quasi Newton method; gender classification

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