# Independent Vector Analysis with more Microphones than Sources

arXiv: https://arxiv.org/abs/1905.07880 · HTML: https://mlsanity.com/paper/1905.07880/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2019.

## Built on by (most cited first, 1 of 1)

- [Generalized Fast Multichannel Nonnegative Matrix Factorization Based on Gaussian Scale Mixtures for Blind Source Separation](https://mlsanity.com/paper/2205.05330/) (2022)

## Abstract (arXiv)

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.