Independent Vector Analysis with more Microphones than Sources
This work addresses a specific bottleneck in audio signal processing for scenarios with redundant microphones, offering an incremental improvement in efficiency.
The paper tackles the problem of blind source separation when there are more microphones than sources by extending independent vector analysis with a parametrized demixing matrix and orthogonal constraints, resulting in separation performance comparable to conventional methods at a significantly lower computational cost.
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.