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In this paper, we present an adaptive sparse factorization method for even-determined and over-determined blind source separation, where the sources are assumed to be sparse. The objective of our method is to find a demixing matrix to make the output signal as sparse as possible. First, a cost function measuring the sparsity of the output signals is introduced. Then an adaptive algorithm for the learning...
An important step of sparse representation technique for under-determined BSS (blind source separation) is the estimation of the mixing matrix. In this paper, a new method to estimate the mixing matrix is proposed. The objective is to find the mixing matrix to minimize the mutual information of the estimated sources. An algorithm for the learning of the mixing matrix is proposed by the natural gradient...
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