LMM based blind signal separation with hybrid sampling

机译：基于LMM的混合采样盲信号分离

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Laplace Mixture Model (LMM) is used to characterize the distribution of observed data. The stationary distribution of the model parameters is obtained by the hybrid of Gibbs and Metropolis-Hastings sampling, by which the mixing matrix is further estimated. With the mixing matrix estimated, we separate the sources successfully. Simulation results show that the method proposed is very robust to initial values and performs better than traditional methods, at the same time the method does not need large calculation amount.

机译：拉普拉斯混合物模型（LMM）用于表征观测数据的分布。模型参数的平稳分布是通过Gibbs和Metropolis-Hastings采样的混合获得的，从而进一步估计了混合矩阵。利用估计的混合矩阵，我们成功地分离了来源。仿真结果表明，该方法对初始值具有很好的鲁棒性，并且比传统方法具有更好的性能，同时不需要大量的计算。

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《Consumer Electronics, Communications and Networks (CECNet), 2012 2nd International Conference on》|2012年|p.1877- 1881|共5页
会议地点 Three Gorges(CN)
作者
Chen Yongqiang;
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Electronic Experiment Center, Chengdu University of Information Technology, Chengdu 610225, China;

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原文格式 PDF
正文语种 eng
中图分类通信;
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LMM based blind signal separation with hybrid sampling

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