Application of multi-objective optimization to blind source separation

Autor(es)

Pelegrina, Guilherme Dean ^[1] ; Attux, Romis ^[2] ; Duarte, Leonardo Tomazeli ^[1]

Afiliação do(s) autor(es)

^[1] Univ Estadual Campinas, Sch Appl Sci, 1300 Pedro Zaccaria St, BR-13484350 Limeira - Brazil

^[2] Univ Estadual Campinas, Sch Elect & Comp Engn, 400 Albert Einstein Ave, BR-13083852 Campinas, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: EXPERT SYSTEMS WITH APPLICATIONS; v. 131, p. 60-70, OCT 1 2019.
• Citações Web of Science: 0

Resumo

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. (AU)

• Processo FAPESP: 14/27108-9 - Métodos de otimização multiobjetivo aplicados a problemas inversos e à separação cega de fontes
• Beneficiário: Guilherme Dean Pelegrina
• Modalidade de apoio: Bolsas no Brasil - Mestrado
• Processo FAPESP: 15/16325-1 - Desenvolvimento de novos métodos de apoio à decisão multicritério utilizando técnicas avançadas de processamento de sinais
• Beneficiário: Leonardo Tomazeli Duarte
• Modalidade de apoio: Auxílio à Pesquisa - Regular