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. The neural network which realizes the separation employs the so called "Mirror Model" and is based on adaptive activation functions, whose shape is properly modified during learning. Nonlinear functions involved in the processing of complex signals are realized by pairs of spline neurons called "splitting functions", working on the real and the imaginary part of the signal respectively. Theoretical proof of existence and uniqueness of the solution under proper assumptions is also provided. In particular a simple adaptation algorithm is derived and some experimental results that demonstrate the effectiveness of the proposed solution are shown.
Flexible nonlinear blind signal separation in the complex domain / Daniele, Vigliano; Scarpiniti, Michele; Parisi, Raffaele; Uncini, Aurelio. - In: INTERNATIONAL JOURNAL OF NEURAL SYSTEMS. - ISSN 0129-0657. - 18:2(2008), pp. 105-122. (Intervento presentato al convegno International Joint Conference on Neural Networks tenutosi a Vancouver, CANADA nel 2006) [10.1142/s0129065708001427].
Flexible nonlinear blind signal separation in the complex domain
SCARPINITI, MICHELE;PARISI, Raffaele;UNCINI, Aurelio
2008
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. The neural network which realizes the separation employs the so called "Mirror Model" and is based on adaptive activation functions, whose shape is properly modified during learning. Nonlinear functions involved in the processing of complex signals are realized by pairs of spline neurons called "splitting functions", working on the real and the imaginary part of the signal respectively. Theoretical proof of existence and uniqueness of the solution under proper assumptions is also provided. In particular a simple adaptation algorithm is derived and some experimental results that demonstrate the effectiveness of the proposed solution are shown.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.