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A novel algorithm based on the analysis sparse constraint of the source over an adaptive dictionary is proposed to solve the blind source separation problem. In the algorithm, the dictionary for each source is adaptively learned from the corresponding source, which is estimated from the mixtures. Moreover, then the analysis sparse representation of the source can be obtained with the learning dictionary...
Underdetermined blind source separation (UBSS) deals with the problem of estimating n source signals from m measurements (n > m), with an unknown mixing process. Most researches pay attention to the sparsity of speech to recover source signals, such as the DUET (degenerate unmixing estimation technique) algorithm, which can separate any number of sources using only two mixtures with the help of...
In this paper, the Potential Function Agglomeration Clustering (PFAC) algorithm has been proposed for estimating the mixing matrix in underdetermined Sparse Component Analysis (SCA), wherein the number of mixtures is less than the number of the sources. In contrast to many existing SCA methods, the PFAC algorithm can accurate estimate the number of sources and the mixing matrix. The algorithm also...
In this paper, a robust K-plane clustering algorithm has been proposed for blind separation of underdetermined mixtures of sparse sources. In the presence of noise, based on the insufficient sparsity assumption of the source signals, the K-dimensional concentration hyperplanes have been found by using the algorithm, and then using them to estimate the mixing matrix. Simulation results show that the...
Most of the proposed algorithms for blind sources separation are not able to extract the source signals when the unknown sources are not mutually statistically independent. In this paper, the blind separation problem for uncorrelated signals is explored. A novel algorithm is proposed based on the nonnegative matrix factorization methods with the least correlated component constraints. The algorithm...
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