This paper introduces an Independent Component Analysis (ICA) approach to the separation of nonlinear mixtures in the complex domain. Source separation is performed by a complex INFOMAX approach. Nonlinear complex functions involved in the processing are realized by pairs of spline neurons called "splitting functions", working on the real and the imaginary part of the signal respectively. A simple adaptation algorithm is derived and some experimental results that demonstrate the effectiveness of the proposed method are shown.
Flexible ICA Approach to the Nonlinear Blind Signal Separation in the Complex Domain / D., Vigliano; Scarpiniti, Michele; Parisi, Raffaele; Uncini, Aurelio. - (2006), pp. 2261-2265. (Intervento presentato al convegno European Signal Processing Conference tenutosi a Florence; Italy nel 04-08/09/2006).
Flexible ICA Approach to the Nonlinear Blind Signal Separation in the Complex Domain
SCARPINITI, MICHELE;PARISI, Raffaele;UNCINI, Aurelio
2006
Abstract
This paper introduces an Independent Component Analysis (ICA) approach to the separation of nonlinear mixtures in the complex domain. Source separation is performed by a complex INFOMAX approach. Nonlinear complex functions involved in the processing are realized by pairs of spline neurons called "splitting functions", working on the real and the imaginary part of the signal respectively. A simple adaptation algorithm is derived and some experimental results that demonstrate the effectiveness of the proposed method are shown.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
