  Rank-constrained spatial covariance matrix estimation (RCSCME) is a
state-of-the-art blind speech extraction method applied to cases where one
directional target speech and diffuse noise are mixed. In this paper, we
proposed a new algorithmic extension of RCSCME. RCSCME complements a deficient
one rank of the diffuse noise spatial covariance matrix, which cannot be
estimated via preprocessing such as independent low-rank matrix analysis, and
estimates the source model parameters simultaneously. In the conventional
RCSCME, a direction of the deficient basis is fixed in advance and only the
scale is estimated; however, the candidate of this deficient basis is not
unique in general. In the proposed RCSCME model, the deficient basis itself can
be accurately estimated as a vector variable by solving a vector optimization
problem. Also, we derive new update rules based on the EM algorithm. We confirm
that the proposed method outperforms conventional methods under several noise
conditions.
