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Flexible Blind Signal Separation in the Complex Domain
"... 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 adapti ..."
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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 socalled “splitting functions”, working on the real and the imaginary part of the signal. In linear environment instead, the “generalized splitting function ” which perform 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.
Flexible ICA in Complex and Nonlinear Environment by Mutual Information Minimization
 In Proc. of IEEE Machine Learning for Signal Processing 2006
, 2006
"... 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 ..."
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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.
A RECURRENT ICA APPROACH TO A NOVEL BSS CONVOLUTIVE NONLINEAR PROBLEM
"... Abstract. This paper introduces a Recurrent Flexible ICA approach to a novel blind sources separation problem in convolutive nonlinear environment. The proposed algorithm performs the separation after the convolutive mixing of post nonlinear convolutive mixtures. The recurrent neural network produce ..."
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Abstract. This paper introduces a Recurrent Flexible ICA approach to a novel blind sources separation problem in convolutive nonlinear environment. The proposed algorithm performs the separation after the convolutive mixing of post nonlinear convolutive mixtures. The recurrent neural network produces the separation by minimizing the output mutual information. Experimental results are described to show the effectiveness of the proposed technique.
FLEXIBLE ICA APPROACH TO THE NONLINEAR BLIND SIGNAL SEPARATION IN THE COMPLEX DOMAIN
"... 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. Nonlinear complex functions involved in the processing are realized by pairs of spline neurons called “s ..."
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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. Nonlinear complex functions involved in the processing are realized by pairs of spline neurons called “splitting functions”, working on the real and the imaginary part of the signal respectively. A simple adaptation algorithm is derived and some experimental results that demonstrate the effectiveness of the proposed method are shown. 1. INTRODUCTITON In the last years Blind Source Separation (BSS) realized through Independent Component Analysis (ICA) have raised great interest in the signal processing community (see e.g.
FLEXIBLE NONLINEAR BLIND SIGNAL SEPARATION IN THE COMPLEX DOMAIN
 INTERNATIONAL JOURNAL OF NEURAL SYSTEM
"... 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 ad ..."
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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.