Joint dictionary learning using a new optimization method for single-channel blind source separation

Abstract Cross projection is often produced between sub-dictionaries when a mixed speech signal is represented over a joint dictionary in single-channel blind source separation (SCBSS), which leads to poor separation performance. To solve this problem, we introduce a new optimization function of joint dictionary learning for SCBSS, which trains the identity sub-dictionaries and common sub-dictionary simultaneously. The existence of a common sub-dictionary can effectively avoid one source signal being represented by the interferer sub-dictionaries. The new optimization function presented in this paper effectively inhibits the cross projection via enforcing constraint and cross-penalty between the identity sub-dictionaries and common sub-dictionary. The optimization of the objective function is introduced in this work, which involves three steps: initial joint dictionary, coding update, and dictionary update. In the speech separation stage, the source separation is achieved by an identity sub-dictionary, the common sub-dictionary and the sparse coefficients of a single mixed signal over the joint dictionary. Experimental results verify that our algorithm works much better than the current algorithms.