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.
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Ghennioui, H., Fadaili, E.M., Thirion-Moreau, N., Adib, A., Moreau, E. (2007). An Algebraic Non Orthogonal Joint Block Diagonalization Algorithm for Blind Separation of Convolutive Mixtures of Sources. In: Davies, M.E., James, C.J., Abdallah, S.A., Plumbley, M.D. (eds) Independent Component Analysis and Signal Separation. ICA 2007. Lecture Notes in Computer Science, vol 4666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74494-8_25
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DOI: https://doi.org/10.1007/978-3-540-74494-8_25
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