title: Flexible nonlinear blind signal separation in the complex domain.
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.
authors.affiliation: INFOCOM Department, Università degli Studi di Roma, Via Eudossiana 18, Roma, Italy 00184, Italy. daniele.vigliano@poste.it
authors.forename: Daniele
authors.initials: D
authors.lastname: Vigliano
authors.forename: Michele
authors.initials: M
authors.lastname: Scarpiniti
authors.forename: Raffaele
authors.initials: R
authors.lastname: Parisi
authors.forename: Aurelio
authors.initials: A
authors.lastname: Uncini
mesh_terms: D000465:Algorithms; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted