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Audio source separation is the task of isolating sound sources that areactive simultaneously in a room captured by a set of microphones. Convolutiveaudio source separation of equal number of sources and microphones has a numberof shortcomings including the complexity of frequency-domain ICA, thepermutation ambiguity and the problem's scalabity with increasing number ofsensors. In this paper, the authors propose a multiple-microphone audio sourceseparation algorithm based on a previous work of Mitianoudis and Davies (2003).Complex FastICA is substituted by Robust ICA increasing robustness andperformance. Permutation ambiguity is solved using two methodologies. The firstis using the Likelihood Ration Jump solution, which is now modified to decreasecomputational complexity in the case of multiple microphones. The applicationof the MuSIC algorithm, as a preprocessing step to the previous solution, formsa second methodology with promising results.

Convolutive Audio Source Separation using Robust ICA and an intelligent evolving permutation ambiguity solution