Blind Source Separation by Sparse Decomposition in a Signal Dictionary
Blind Source Separation by Sparse Decomposition in a Signal Dictionary
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DOI：
10.1162/089976601300014385
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发表时间：
2001-04
影响因子：
2.9
通讯作者：
M. Zibulevsky;Barak A. Pearlmutter
中科院分区：
文献类型：
--
作者：
M. Zibulevsky;Barak A. Pearlmutter
盲源分离问题是从一组线性混合信号中提取出潜在的源信号，其中混合矩阵是未知的。这种情况在声学、无线电、医学信号和图像处理、高光谱成像和其他领域中很常见。我们建议一个两阶段的分离过程：先验选择一个可能过完备的信号字典（例如，小波框架或学习字典），其中的源被假定为稀疏表示，其次是解混的源，利用其稀疏表示。我们认为一般情况下，更多的来源比混合物，但也得到一个更有效的算法的情况下，一个nonovercomplete字典和相等数量的来源和混合物。人工信号和音乐声音的实验证明了比其他已知技术更好的分离。
The blind source separation problem is to extract the underlying source signals from a set of linear mixtures, where the mixing matrix is unknown. This situation is common in acoustics, radio, medical signal and image processing, hyperspectral imaging, and other areas. We suggest a two-stage separation process: a priori selection of a possibly overcomplete signal dictionary (for instance, a wavelet frame or a learned dictionary) in which the sources are assumed to be sparsely representable, followed by unmixing the sources by exploiting the their sparse representability. We consider the general case of more sources than mixtures, but also derive a more efficient algorithm in the case of a nonovercomplete dictionary and an equal numbers of sources and mixtures. Experiments with artificial signals and musical sounds demonstrate significantly better separation than other known techniques.