Underdetermined Blind Source Separation Based on Sparse Component

机译：基于稀疏组件的未确定的盲源分离

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摘要

This paper presents a new algorithm to identify matrix A ∈ R~(m×n) knowing only their multiplication X = AS. Where S ∈ R~(n×N) is sparse and m < n. The data used for matrix identification are chosen by Least Square method, whose fitting errors are smaller than a given threshold. Then, K-means clustering method is adopted. This technique avoids data overlapping at the origin, thus improving the accuracy of mixing matrix estimation. The validity of the method for true voice separation is verified by computer simulation. Also comparison with other methods is made to verify the efficiency of the algorithm. Simulations show that the algorithm has the property of accuracy and low-cost computation.

机译：本文介绍了一个新的算法，用于识别矩阵A∈R〜（m×n）只知道它们的乘法x = ina。其中s∈r〜（n×n）稀疏，m

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《International Conference on Electronic Computer Technology》|2009年||共4页
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Ming-rong Ren; Pu Wang;
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中图分类 TP3-53;
关键词
Blind source separation (BSS); Underdetermined mixtures; Sparse component analysis (SCA); Clustering; Least square;

机译：盲源分离（BSS）;未确定的混合物;稀疏分量分析（SCA）;聚类;最小二乘;

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3. Empirical kernel map approach to nonlinear underdetermined blind separation of sparse nonnegative dependent sources: pure component extraction from nonlinear mixture mass spectra [J] . Kopriva Ivica, Jeric Ivanka, Filipovic Marko, Journal of Chemometrics . 2014,第9期

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5. System approach to robust acoustic echo cancellation through semi-blind source separation based on independent component analysis. [D] . Wada, Ted S. 2012

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Underdetermined Blind Source Separation Based on Sparse Component

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