Clasificación de género basada en señales de voz mediante modelos difusos y algoritmos de optimización
DOI:
https://doi.org/10.15332/iteckne.v16i2.2356Palabras clave:
Lógica difusa, optimización, algoritmos genéticos, búsqueda armónica, evolución diferencial, optimización con enjambre de partículas, método cuasi-Newton, clasificación de géneroResumen
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.
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