Time-Frequency Approach to Underdetermined Blind Source Separation
Time-Frequency Approach to Underdetermined Blind Source Separation
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DOI：
10.1109/tnnls.2011.2177475
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发表时间：
2012-01
影响因子：
10.4
通讯作者：
S. Xie;Liu Yang;Jun-Mei Yang;Guoxu Zhou;Yong Xiang
中科院分区：
文献类型：
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作者：
S. Xie;Liu Yang;Jun-Mei Yang;Guoxu Zhou;Yong Xiang
提出了一种新的基于Wigner-Ville分布（WVD）和Khatri-Rao乘积的时频欠定盲分离方法，从M（M <; N）个混合信号中分离出N个非平稳信号。首先，提出了一种改进的混合矩阵估计方法，充分考虑了信源的自WVD为负值的情况。在提取出所有自项TF点后，只要N ≤ 2 M-1，无论有多少个活动源，该方法都能准确地求出每个自项TF点处源的自WVD值。最后通过数值仿真实验，与现有算法进行了比较，验证了该算法的优越性。
This paper presents a new time-frequency (TF) underdetermined blind source separation approach based on Wigner-Ville distribution (WVD) and Khatri-Rao product to separate N non-stationary sources from M(M <; N) mixtures. First, an improved method is proposed for estimating the mixing matrix, where the negative value of the auto WVD of the sources is fully considered. Then after extracting all the auto-term TF points, the auto WVD value of the sources at every auto-term TF point can be found out exactly with the proposed approach no matter how many active sources there are as long as N ≤ 2M-1. Further discussion about the extraction of auto-term TF points is made and finally the numerical simulation results are presented to show the superiority of the proposed algorithm by comparing it with the existing ones.