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We study an efficient dynamic blind source separation algorithm ofconvolutive sound mixtures based on updating statistical information in thefrequency domain, andminimizing the support of time domain demixing filters bya weighted least square method. The permutation and scaling indeterminacies ofseparation, and concatenations of signals in adjacent time frames are resolvedwith optimization of $l^1 \times l^\infty$ norm on cross-correlationcoefficients at multiple time lags. The algorithm is a direct method withoutiterations, and is adaptive to the environment. Computations on recorded andsynthetic mixtures of speech and music signals show excellent performance.

A Dynamic Algorithm for Blind Separation of Convolutive Sound Mixtures