  We study an efficient dynamic blind source separation algorithm of
convolutive sound mixtures based on updating statistical information in the
frequency domain, andminimizing the support of time domain demixing filters by
a weighted least square method. The permutation and scaling indeterminacies of
separation, and concatenations of signals in adjacent time frames are resolved
with optimization of $l^1 \times l^\infty$ norm on cross-correlation
coefficients at multiple time lags. The algorithm is a direct method without
iterations, and is adaptive to the environment. Computations on recorded and
synthetic mixtures of speech and music signals show excellent performance.
