  In this paper, we propose a new algorithm that efficiently separates a
directional source and diffuse background noise based on independent low-rank
matrix analysis (ILRMA). ILRMA is one of the state-of-the-art techniques of
blind source separation (BSS) and is based on a rank-1 spatial model. Although
such a model does not hold for diffuse noise, ILRMA can accurately estimate the
spatial parameters of the directional source. Motivated by this fact, we
utilize these estimates to restore the lost spatial basis of diffuse noise,
which can be considered as an efficient full-rank spatial covariance
estimation. BSS experiments show the efficacy of the proposed method in terms
of the computational cost and separation performance.
