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Type: Artigo
Title: Application of multi-objective optimization to blind source separation
Author: Pelegrina, Guilherme Dean
Attux, Romis
Duarte, Leonardo Tomazeli
Abstract: Several problems in signal processing are addressed by expert systems which take into account a set of priors on the sought signals and systems. For instance, blind source separation is often tackled by means of a mono-objective formulation which relies on a separation criterion associated with a given property of the sought signals (sources). However, in many practical situations, there are more than one property to be exploited and, as a consequence, a set of separation criteria may be used to recover the original signals. In this context, this paper addresses the separation problem by means of an approach based on multi-objective optimization. Differently from the existing methods, which provide only one estimate for the original signals, our proposal leads to a set of solutions that can be utilized by the system user to take his/her decision. Results obtained through numerical experiments over a set of biomedical signals highlight the viability of the proposed approach, which provides estimations closer to the mean squared error solutions compared to the ones achieved via a mono-objective formulation. Moreover, since our proposal is quite general, this work also contributes to encourage future researches to develop expert systems that exploit the multi-objective formulation in different source separation problems
Subject: Algoritmos evolutivos
Country: Reino Unido
Editor: Elsevier
Rights: Fechado
Identifier DOI: 10.1016/j.eswa.2019.04.041
Date Issue: Oct-2019
Appears in Collections:FCA - Artigos e Outros Documentos

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