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This paper proposes to use a radial basis function (RBF) network to increase the separation performance of blind signal separation (BSS). Independent component analysis (ICA) is often used for BSS, but in general, ICA employs a sigmoid function to describe the probability distribution of signals in the process of learning. We attempt to describe the probability distribution of signals as accurately as possible in order to improve the performance of signal separation by ICA. The proposed method is applied to the signal separation problem of actual speech signals. The effectiveness of the proposed method has been confirmed by simulation experiments.