  In this paper, we address the problem of extracting all super-Gaussian source
signals from a linear mixture in which (i) the number of super-Gaussian sources
$K$ is less than that of sensors $M$, and (ii) there are up to $M - K$
stationary Gaussian noises that do not need to be extracted. To solve this
problem, independent vector extraction (IVE) using a majorization minimization
and block coordinate descent (BCD) algorithms has been developed, attaining
robust source extraction and low computational cost. We here improve the
conventional BCDs for IVE by carefully exploiting the stationarity of the
Gaussian noise components. We also newly develop a BCD for a semiblind IVE in
which the transfer functions for several super-Gaussian sources are given a
priori. Both algorithms consist of a closed-form formula and a generalized
eigenvalue decomposition. In a numerical experiment of extracting speech
signals from noisy mixtures, we show that when $K = 1$ in a blind case or at
least $K - 1$ transfer functions are given in a semiblind case, the convergence
of our proposed BCDs is significantly faster than those of the conventional
ones.
