Spatially Informed Independent Vector Analysis

Abstract

We present a Maximum A Posteriori (MAP) derivation of the Independent Vector Analysis (IVA) algorithm, a blind source separation algorithm, by incorporating a prior over the demixing matrices, relying on a free-field model. In this way, the outer permutation ambiguity of IVA is avoided. The resulting MAP optimization problem is solved by deriving majorize-minimize update rules to achieve convergence speed comparable to the well-known auxiliary function IVA algorithm. The performance of the proposed algorithm is investigated and compared to a benchmark algorithm using real measurements.