A new mixing matrix estimation method for under-determined blind source separation of audio signals is proposed. By statistically modeling the local features, i.e., the magnitude ratio and phase difference of the mixtures, in a time-frequency region, a region can have information of the mixing angle of a source with reliability amounted to its likelihood. Regional data are then clustered with statistical tests based on their likelihood to produce estimates for the mixing angle of the sources as well as the number of them. Experimental results show that the proposed mixing matrix estimation algorithm outperform the existing methods.
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