Independent Low-Rank Matrix Analysis Based on Parametric Majorization-Equalization Algorithm
Abstract
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.
Cite
@article{arxiv.1710.01589,
title = {Independent Low-Rank Matrix Analysis Based on Parametric Majorization-Equalization Algorithm},
author = {Yoshiki Mitsui and Daichi Kitamura and Norihiro Takamune and Hiroshi Saruwatari and Yu Takahashi and Kazunobu Kondo},
journal= {arXiv preprint arXiv:1710.01589},
year = {2017}
}
Comments
Preprint Manuscript of 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2017)