Independent vector analysis with a multivariate generalized gaussian source prior for frequency domain blind source separation

Abstract

The independent vector analysis (IVA) algorithm can theoretically avoid the permutation problem in frequency domain blind source separation by using a multivariate source prior to retain the dependency between different frequency bins of each source. In this paper, a new multivariate generalized Gaussian distribution is adopted as the source prior which can exploit fourth order inter-frequency correlation, and therefore better preserve the dependency between different frequency bins to achieve an improved separation performance as compared with the original IVA algorithm. Separation performances are compared by simulation studies when using different source priors, and the experimental results confirm that IVA with the new source prior can consistently achieve improved separation performance.