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- W2066373778 abstract "This paper proposes a method of “blind separation” which extracts non-stationary signals (e.g., speech signals, music) from their convolutive mixtures (observed signals). The function is acquired by modifying a network's parameters so that a cost function takes the minimum at any time. The cost function is the one introduced by Matsuoka et al. (Neural Networks 8 (3) (1995) 411–419). The learning rule is derived from the natural gradient (Amari et al., 1998, IEEE Trans. Signal Processing, submitted) minimization of the cost function. The validity of the proposed method is confirmed by computer simulation. The proposed method is applied to the case of No (the number of observed signals)=Ns (the number of source signals) and No>Ns." @default.
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- W2066373778 date "1998-11-01" @default.
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- W2066373778 title "A method of blind separation for convolved non-stationary signals" @default.
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- W2066373778 doi "https://doi.org/10.1016/s0925-2312(98)00055-1" @default.
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