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首页> 外文会议>Youth Academic Annual Conference of Chinese Association of Automation >Local mean decomposition for source separation in underdetermined mixing model
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Local mean decomposition for source separation in underdetermined mixing model

机译：有未确定的混合模型中的源分离的局部平均分解

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Most of the existing underdetermined blind source separation (BSS) approaches assume that the source signals are strictly or partially sparse. This paper, however, presents a BSS method in underdetermined mixing situation for non-sparse signals based on the local mean decomposition (LMD) algorithm. The BSS method firstly introduces LMD into the BSS problem to rebuild a few extra mixing signals. Such signals are then combined with the initial mixtures such that the underdetermined BSS problem is transformed into a determined one and the difficulty of the deficiency of the mixtures is overcome. For the rebuilt mixtures and the newly formed determined BSS problem, two BSS algorithms are proposed to recover the sources. One algorithm uses singular value decomposition on the second-order statistics matrix of the new mixtures to realize the separation, and the other employs an independent component analysis (ICA) type BSS algorithm with stable Frobenius norm constraint on the separating matrix. The simulation results have demonstrated that the proposed underdetermined BSS algorithms can process non-sparse signals, and can acquire a nearly 3dB lower mean square error than the previous non-sparse BSS algorithm.
机译：大多数现有的未确定盲源分离（BSS）方法假设源信号严格或部分稀疏。然而，本文在基于局部平均分解（LMD）算法的非稀疏信号的未稀疏信号的未被稀疏信号中提出了BSS方法。 BSS方法首先将LMD引入BSS问题以重建几个额外的混合信号。然后将这种信号与初始混合物组合，使得未定名的BSS问题被转化为确定的BSS问题，并且克服了混合物的缺陷的难度。对于重建混合和新形成的BSS问题，提出了两个BSS算法以恢复源。一种算法在新混音的二阶统计矩阵上使用奇异值分解来实现分离，另一个算法在分离中采用独立的分量分析（ICA）型BSS算法，在分离矩阵上具有稳定的Frobenius规范约束。仿真结果表明，所提出的未确定的BSS算法可以处理非稀疏信号，并且可以从先前的非稀疏BSS算法获取近3DB的低平均方误码。

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