Adaptive independent vector analysis for the separation of convoluted mixtures using EM algorithm

This paper presents a novel adaptive approach to the separation of convolutedly mixed acoustic signals based on independent vector analysis (IVA). IVA, as an extension of independent component analysis (ICA) from univariate components to multivariate components, provides an efficient framework for avoiding the well-known permutation problem in frequency-domain blind source separation (BSS). However, since IVA has been mostly employing pre-specified and simple source priors which are good fits to speech signals, the performance degrades when the mixture includes unknown sources other than speech. Also, sensor noise has not been considered. To tackle these limitations, we employ multivariate Gaussian mixture model (GMM) as the source priors and add sensor noise into the model. We derive an expectation maximization (EM) algorithm that estimates the separating matrices and the parameters of the unknown source prior together. The performance is demonstrated by experimental results that include the comparison with the IVA results using fixed source priors.