This paper introduces an Independent Component Analysis (ICA) approach to the separation of nonlinear mixtures in the complex domain. Source separation is performed by the minimization of output mutual information (MMI approach). Nonlinear complex functions involved in the processing are realized by the so called “splitting functions” which work on the real and the imaginary part of the signal respectively. Some experimental results that demonstrate the effectiveness of the proposed method are shown.
Flexible ICA in Complex and Nonlinear Environment by Mutual Information Minimization / Vigliano, D; Scarpiniti, Michele; Parisi, Raffaele; Uncini, Aurelio. - (2006), pp. 59-64. (Intervento presentato al convegno IEEE workshop on machine learning for signal processing tenutosi a Maynooth, Ireland nel 6-8/09/2006) [10.1109/MLSP.2006.275522].
Flexible ICA in Complex and Nonlinear Environment by Mutual Information Minimization
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 the minimization of output mutual information (MMI approach). Nonlinear complex functions involved in the processing are realized by the so called “splitting functions” which work on the real and the imaginary part of the signal respectively. 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.
