A Joint Second-Order Statistics and Density Matching-Based Approach for Separation of Post-Nonlinear Mixtures

Autor(es)

Fantinato, Denis G. ; Duarte, Leonardo T. ; Zanini, Paolo ; Rivet, Bertrand ; Attux, Romis ; Jutten, Christian ; Tichavsky, P ; BabaieZadeh, M ; Michel, OJJ ; ThirionMoreau, N

• Tipo de documento: Artigo Científico
• Fonte: LATENT VARIABLE ANALYSIS AND SIGNAL SEPARATION (LVA/ICA 2017); v. 10169, p. 10-pg., 2017-01-01.

Resumo

In the context of Post-Nonlinear (PNL) mixtures, source separation can be performed in a two-stage approach, which encompasses a nonlinear and a linear compensation part. In the former part, it is usually required the knowledge of all the source distributions. In this work, we propose a less restrictive approach, where only one source distribution is needed to be known-here, chosen to be a colored Gaussian. The other sources are only required to present a time structure. The method combines, in a joint-based approach, the use of the second-order statistics (SOS) and the matching of distributions, which shows to be less costly than the classical method of computing the marginal entropy for all sources. The simulation results are favorable to the proposal. (AU)

• Processo FAPESP: 15/23424-6 - Separação Cega de Fontes Não Linear no Contexto de Fontes Estatisticamente Dependentes
• Beneficiário: Denis Gustavo Fantinato
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Doutorado
• Processo FAPESP: 13/14185-2 - Novas Metodologias de Aprendizado Baseado na Teoria da Informação para Equalização Adaptativa
• Beneficiário: Denis Gustavo Fantinato
• Modalidade de apoio: Bolsas no Brasil - Doutorado