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• W2162646593 abstract "This paper proposes a new efficient multichannel nonnegative matrix factorization (NMF) method. Recently, multichannel NMF (MNMF) has been proposed as a means of solving the blind source separation problem. This method estimates a mixing system of sources and attempts to separate them in a blind fashion. However, this method is strongly dependent on its initial values because there are no constraints in the spatial models. To solve this problem, we introduce a rank-1 spatial model into MNMF. The proposed method estimates a demixing matrix while representing sources using NMF bases and can be optimized by the update rules of independent vector analysis and conventional single-channel NMF. Experimental results show the efficacy of the proposed method in terms of robustness and convergence speed." @default.
• W2162646593 created "2016-06-24" @default.
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• W2162646593 date "2015-04-01" @default.
• W2162646593 modified "2023-10-03" @default.
• W2162646593 title "Efficient multichannel nonnegative matrix factorization exploiting rank-1 spatial model" @default.
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• W2162646593 doi "https://doi.org/10.1109/icassp.2015.7177975" @default.
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