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Convolutive Audio Source Separation Using Robust ICA and Reduced Likelihood Ratio Jump

Abstract : Audio source separation is the task of isolating sound sources that are active simultaneously in a room captured by a set of microphones. Convolutive audio source separation of equal number of sources and microphones has a number of shortcomings including the complexity of frequency-domain ICA, the permutation ambiguity and the problem’s scalabity with increasing number of sensors. In this paper, the authors propose a multiple-microphone audio source separation algorithm based on a previous work of Mitianoudis and Davies [1]. Complex FastICA is substituted by Robust ICA increasing robustness and performance. Permutation ambiguity is solved using the Likelihood Ration Jump solution, which is now modified to decrease computational complexity in the case of multiple microphones.
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Submitted on : Thursday, July 6, 2017 - 1:55:04 PM
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Dimitrios Mallis, Thomas Sgouros, Nikolaos Mitianoudis. Convolutive Audio Source Separation Using Robust ICA and Reduced Likelihood Ratio Jump. 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2016, Thessaloniki, Greece. pp.230-241, ⟨10.1007/978-3-319-44944-9_20⟩. ⟨hal-01557598⟩



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