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espanolCon el fin de aumentar la eficiencia del Analisis de Componentes Independientes (ICA) y reducir la complejidad computacional del sistema, este articulo propone una metodologia basada en la tecnica de muestreo compresivo, usando mediciones minimas. Esta permite comprimir y modificar las caracteristicas Gaussianas de las senales de audio. ICA es uno de los esquemas mas utilizados para la separacion a ciegas de fuentes (BSS), a partir unicamente de las mezclas recibidas en un conjunto de sensores. Sin embargo, el ICA requiere que las senales involucradas no sean de tipo Gaussiano, o solamente una de ellas lo sea, caracteristicas que no satisfacen las senales de audio, las cuales son en general de tipo Gaussiano. La metodologia propuesta permite obtener la separacion de las senales en forma mas eficiente, con una menor complejidad computacional, aun cuando todas ellas sean de tipo Gaussiano. EnglishTo increase the efficiency of the independent component analysis (ICA) and reduce the computational complexity of the system, this paper proposes a methodology based on the compressive sampling with minimum measurements. This allows compressing and modifying the Gaussian characteristics of audio signals. ICA is one of the most widely used schemes for separating the sources involved in an audio mixture received in a set of sensors. However, for proper operation it is required that the signals involved in the mixture do not have Gaussian characteristics or at most only one of them be Gaussian, characteristics that are not satisfied by the audio signals, which have in general Gaussian characteristics. The proposed methodology allows obtaining a better separation with lower computational complexity, and with a more efficient separation of the mixed signals, even if the signals involved in the mixture are of Gaussian toe.

Separación de señales usando análisis de componentes principales y muestreo compresivo con mediciones mínimas