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• W2003203076 abstract "We propose a novel clustering technique based on a relaxed disjointness assumption for joint blind source separation (BSS) and dere-verberation. A disjointness assumption in conventional clustering techniques for BSS is that, at each time-frequency point, observed mixtures consist of a single source only. However, this is not the case in reverberant environments, which causes the performance of the conventional techniques to degrade. To deal with reverberant environments, we introduce a relaxed disjointness assumption: at each time-frequency point, dereverberated mixtures consist of a single source only. Under this assumption, the proposed algorithm alternates dereverberation and clustering-based source separation it-eratively, where clustering is performed on dereverberated mixtures. This algorithm is derived based on maximum a posteriori (MAP) fitting of a probabilistic generative model to observed reverberant mixtures. In experiments, the proposed method outperformed a state-of-the-art clustering technique in terms of a signal-to-interference ratio (SIR) by 0.6–4 dB." @default.
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• W2003203076 date "2014-09-01" @default.
• W2003203076 modified "2023-10-05" @default.
• W2003203076 title "Relaxed disjointness based clustering for joint blind source separation and dereverberation" @default.
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• W2003203076 doi "https://doi.org/10.1109/iwaenc.2014.6954300" @default.
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