  We address a blind source separation (BSS) problem in a noisy reverberant
environment in which the number of microphones $M$ is greater than the number
of sources of interest, and the other noise components can be approximated as
stationary and Gaussian distributed. Conventional BSS algorithms for the
optimization of a multi-input multi-output convolutional beamformer have
suffered from a huge computational cost when $M$ is large. We here propose a
computationally efficient method that integrates a weighted prediction error
(WPE) dereverberation method and a fast BSS method called independent vector
extraction (IVE), which has been developed for less reverberant environments.
We show that, given the power spectrum for each source, the optimization
problem of the new method can be reduced to that of IVE by exploiting the
stationary condition, which makes the optimization easy to handle and
computationally efficient. An experiment of speech signal separation shows
that, compared to a conventional method that integrates WPE and independent
vector analysis, our proposed method achieves much faster convergence while
maintaining its separation performance.
