In this paper, a novel use of adaptive filters for blind source separation is presented. The known independent component analysis algorithm separates signals from their mixtures based on the observation that a mixture of statistically independent signals is more Gaussian than the separate signals. Similarly, an adaptive filter, that is designed to minimize the Gaussianity of its output, relies on the same hypothesis. The proposed adaptive filter uses all the mixture signals, the observation signals, as its inputs—one as the main input, and the rest as reference inputs. The filter is iteratively modified, using gradient descent, such that the measure of non-Gaussianity of its output is maximized, leading to the separation of one source signal at its output. To separate the N source signals from the given N mixtures, N such adaptive filters are used. The proposed method has been successfully applied to the blind separation of multiple signals.

中文翻译：

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用于盲源分离的多重最小均值峰态自适应滤波器

在本文中，提出了一种用于盲源分离的自适应滤波器的新用途。已知的独立分量分析算法基于观察到统计独立信号的混合比单独的信号更具有高斯性而从它们的混合中分离信号。类似地，旨在最小化其输出的高斯性的自适应滤波器依赖于相同的假设。所提出的自适应滤波器使用所有混合信号、观测信号作为其输入——一个作为主要输入，其余作为参考输入。使用梯度下降对滤波器进行迭代修改，使其输出的非高斯性度量最大化，从而导致在其输出处分离一个源信号。为了从给定的 N 个混合中分离 N 个源信号，使用了 N 个这样的自适应滤波器。