Spatially Guided Independent Vector Analysis

We present a Maximum A Posteriori (MAP) derivation of the Independent Vector Analysis (IVA) algorithm for blind source separation incorporating an additional spatial prior over over the demixing matrices. In this way, the outer permutation ambiguity of IVA is avoided and the algorithm can be guided towards a desired solution in adverse acoustic conditions. 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, i.e., the convergence is not impaired by the additional constraint. The proposed algorithm exhibits superior performance at lower computational cost than a state-of-the-art spatially constrained IVA algorithm in a setup defined by real-world Room Impulse Responses (RIRs).