  We address the convolutive blind source separation problem for the
(over-)determined case where (i) the number of nonstationary target-sources $K$
is less than that of microphones $M$, and (ii) there are up to $M - K$
stationary Gaussian noises that need not to be extracted. Independent vector
analysis (IVA) can solve the problem by separating into $M$ sources and
selecting the top $K$ highly nonstationary signals among them, but this
approach suffers from a waste of computation especially when $K \ll M$. Channel
reductions in preprocessing of IVA by, e.g., principle component analysis have
the risk of removing the target signals. We here extend IVA to resolve these
issues. One such extension has been attained by assuming the orthogonality
constraint (OC) that the sample correlation between the target and noise
signals is to be zero. The proposed IVA, on the other hand, does not rely on OC
and exploits only the independence between sources and the stationarity of the
noises. This enables us to develop several efficient algorithms based on block
coordinate descent methods with a problem specific acceleration. We clarify
that one such algorithm exactly coincides with the conventional IVA with OC,
and also explain that the other newly developed algorithms are faster than it.
Experimental results show the improved computational load of the new algorithms
compared to the conventional methods. In particular, a new algorithm
specialized for $K = 1$ outperforms the others.
