A Flexible Natural Gradient Approach to Blind Separation of Complex Signals

Authors:
Michele Scarpiniti;Daniele Vigliano;Raffaele Parisi;Aurelio Uncini
Affiliations:
Infocom Department, “Sapienza” University of Rome;Infocom Department, “Sapienza” University of Rome;Infocom Department, “Sapienza” University of Rome;Infocom Department, “Sapienza” University of Rome
Venue:
Proceedings of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008
Year:
2009

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Abstract

In this paper a natural gradient approach to blind source separation in complex environment is presented. It is shown that signals can be successfully reconstructed by a network based on the so called generalized splitting activation function (GSAF). This activation function, whose shape is modified during the learning process, is based on a couple of bi-dimensional spline functions, one for the real and one for the imaginary part of the input, thus avoiding the restriction due to the Louiville's theorem. In addition recent learning metrics are compared with the classical ones in order to improve the speed convergence. Several experimental results are shown to demonstrate the effectiveness of the proposed method.