Application of multi-objective optimization to blind source separation

Guilherme Dean Pelegrina

doi:10.1016/j.eswa.2019.04.041

Return

Application of multi-objective optimization to blind source separation

2019-10-01

Guilherme Dean Pelegrina *

Romis Attux

Leonardo Tomazeli Duarte

DOI：10.1016/j.eswa.2019.04.041

Original

Save

View PDF

Abstract

En 中文

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. (C) 2019 Elsevier Ltd. All rights reserved.

Keywords:

Blind source separation

Multi-objective optimization

Evolutionary algorithms