An Algebraic Non Orthogonal Joint Block Diagonalization Algorithm for Blind Separation of Convolutive Mixtures of Sources

Abstract

This paper deals with the problem of the blind separation of convolutive mixtures of sources. We present a novel method based on a new non orthogonal joint block diagonalization algorithm (NO − JBD) of a given set of matrices. The main advantages of the proposed method are that it is more general and a preliminary whitening stage is no more compulsorily required. The proposed joint block diagonalization algorithm is based on the algebraic optimization of a least mean squares criterion. Computer simulations are provided in order to illustrate the effectiveness of the proposed approach in three cases: when exact block-diagonal matrices are considered, then when they are progressively perturbed by an additive Gaussian noise and finally when estimated correlation matrices are used. A comparison with a classical orthogonal joint block-diagonalization algorithm is also performed, emphasizing the good performances of the method.KeywordsPerformance IndexCorrelation MatriceSource SeparationBlind Source SeparationAdditive Gaussian NoiseThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.