Blind separation of convolutive mixtures over Galois fields

Author

Fantinato, Denis G. ; Silva, Daniel G. ; Nadalin, Everton Z. ; Attux, Romis ; Romano, Joao Marcos Travassos ; Neves, Aline ; Montalvao, J.

Author_Institution

Univ. of Campinas - UNICAMP, Campinas, Brazil

fYear

2013

fDate

22-25 Sept. 2013

Firstpage

1

Lastpage

6

Abstract

The efforts of Yeredor, Gutch, Gruber and Theis have established a theory of blind source separation (BSS) over finite fields that can be applied to linear and instantaneous mixing models. In this work, the problem is treated for the case of convolutive mixtures, for which the process of BSS must be understood in terms of space-time processing. A method based on minimum entropy and deflation is proposed, and structural conditions for perfect signal recovery are defined, establishing interesting points of contact with canonical MIMO equalization. Simulation results give support to the applicability of the proposed algorithm and also reveal the important role of efficient entropy estimation when the complexity of the mixing system is increased.

Keywords

blind source separation; convolution; entropy; BSS theory; Galois fields; blind source separation; canonical MIMO equalization; convolutive mixtures; deflation; entropy estimation; instantaneous mixing models; linear mixing models; minimum entropy; mixing system complexity; perfect signal recovery; space-time processing; Entropy; Estimation; Finite impulse response filters; Galois fields; IIR filters; MIMO; Source separation; Blind source separation; Galois fields; convolutive mixtures;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning for Signal Processing (MLSP), 2013 IEEE International Workshop on

Conference_Location

Southampton

ISSN

1551-2541

Type

conf

DOI

10.1109/MLSP.2013.6661908

Filename

6661908