Flexible Blind Signal Separation in the Complex Domain

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Author(s): Michele Scarpiniti (University of Rome “La Sapienza”, Italy), Daniele Vigliano (University of Rome “La Sapienza”, Italy), Raffaele Parisi (University of Rome “La Sapienza”, Italy)and Aurelio Uncini (University of Rome “La Sapienza”, Italy)
Copyright: 2009
Pages: 40
Source title: Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters
Source Author(s)/Editor(s): Tohru Nitta (National Institute of Advanced Industrial Science and Technology, Japan)
DOI: 10.4018/978-1-60566-214-5.ch012

Keywords: Artificial Intelligence / Computer Science & IT / Information Science Reference / Neural Networks

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Abstract

This chapter aims at introducing an Independent Component Analysis (ICA) approach to the separation of linear and nonlinear mixtures in complex domain. Source separation is performed by an extension of the INFOMAX approach to the complex environment. The neural network approach is based on an adaptive activation function, whose shape is properly modified during learning. Different models have been used to realize complex nonlinear functions for the linear and the nonlinear environment. In nonlinear environment the nonlinear functions involved during the learning are implemented by the so-called splitting functions, working on the real and the imaginary part of the signal. In linear environment instead, the generalized splitting function which performs a more complete representation of complex function is used. Moreover a simple adaptation algorithm is derived and several experimental results are shown to demonstrate the effectiveness of the proposed method.

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Flexible Blind Signal Separation in the Complex Domain

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