  We extend frequency-domain blind source separation based on independent
vector analysis to the case where there are more microphones than sources. The
signal is modelled as non-Gaussian sources in a Gaussian background. The
proposed algorithm is based on a parametrization of the demixing matrix
decreasing the number of parameters to estimate. Furthermore, orthogonal
constraints between the signal and background subspaces are imposed to
regularize the separation. The problem can then be posed as a constrained
likelihood maximization. We propose efficient alternating updates guaranteed to
converge to a stationary point of the cost function. The performance of the
algorithm is assessed on simulated signals. We find that the separation
performance is on par with that of the conventional determined algorithm at a
fraction of the computational cost.
