Blind source separation using analysis sparse constraint

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. The representation of the source is the constraint that can be employed to extract the source from the mixtures. Experimental results demonstrate that the proposed method improves separation performance.