  In this paper, we propose a new optimization method for independent low-rank
matrix analysis (ILRMA) based on a parametric majorization-equalization
algorithm. ILRMA is an efficient blind source separation technique that
simultaneously estimates a spatial demixing matrix (spatial model) and the
power spectrograms of each estimated source (source model). In ILRMA, since
both models are alternately optimized by iterative update rules, the difference
in the convergence speeds between these models often results in a poor local
solution. To solve this problem, we introduce a new parameter that controls the
convergence speed of the source model and find the best balance between the
optimizations in the spatial and source models for ILRMA.
