  We revisit the source image estimation problem from blind source separation
(BSS). We generalize the traditional minimum distortion principle to maximum
likelihood estimation with a model for the residual spectrograms. Because
residual spectrograms typically contain other sources, we propose to use a
mixed-norm model that lets us finely tune sparsity in time and frequency. We
propose to carry out the minimization of the mixed-norm via
majorization-minimization optimization, leading to an iteratively reweighted
least-squares algorithm. The algorithm balances well efficiency and ease of
implementation. We assess the performance of the proposed method as applied to
two well-known determined BSS and one joint BSS-dereverberation algorithms. We
find out that it is possible to tune the parameters to improve separation by up
to 2 dB, with no increase in distortion, and at little computational cost. The
method thus provides a cheap and easy way to boost the performance of blind
source separation.
