Vectorwise Coordinate Descent Algorithm for Spatially Regularized Independent Low-Rank Matrix Analysis
Yoshiki Mitsui, Norihiro Takamune, Daichi Kitamura, Hiroshi Saruwatari, Yu Takahashi, Kazunobu Kondo
Abstract
Audio source separation is an important problem for many audio applications. Independent low-rank matrix analysis (ILRMA) is a recently proposed algorithm that employs the statistical independence between sources and the low-rankness of the time-frequency structure in each source. As reported in this paper, we have developed a new framework that enables us to introduce a spatial regularization of the demixing matrix in ILRMA. Since the conventional optimization cannot be applied to this regularized ILRMA, we derive a novel approach based on vectorwise coordinate descent, which does not require a step-size parameter and guarantees convergence. In experiments, ILRMA with beamforming-based regularization is evaluated as an application of the proposed framework.
BibTeX
@inproceedings{icassp2018_vectorwisecoordi,
title = {Vectorwise Coordinate Descent Algorithm for Spatially Regularized Independent Low-Rank Matrix Analysis},
author = {Yoshiki Mitsui and Norihiro Takamune and Daichi Kitamura and Hiroshi Saruwatari and Yu Takahashi and Kazunobu Kondo},
booktitle = {ICASSP 2018},
year = {2018}
}