Convolutive Blind Source Separation in the Frequency Domain Based on Sparse Representation
Convolutive Blind Source Separation in the Frequency Domain Based on Sparse Representation
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DOI：
10.1109/tasl.2007.898457
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发表时间：
2007-07
期刊：
影响因子：
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通讯作者：
Zhaoshui He;S. Xie;Shuxue Ding;A. Cichocki
中科院分区：
文献类型：
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作者：
Zhaoshui He;S. Xie;Shuxue Ding;A. Cichocki
卷积盲源分离（CBSS）是利用源信号在频域的稀疏性。我们假设源信号服从复随机变量的类拉普拉斯分布，其中复值源信号的真实的部分和虚部分不一定独立。基于最大后验概率（MAP）准则，提出了一种新的自然梯度法。此外，一个新的CBSS方法进一步发展的基础上，复稀疏表示。所开发的CBSS算法在频域中工作。这里，我们假设源信号在频域中是足够稀疏的。如果源信号在频域中足够稀疏，并且混合通道的滤波器长度相对较小并且可以估计，我们甚至可以实现欠定CBSS。我们通过几个仿真例子说明了所提出的学习算法的有效性和性能。
Convolutive blind source separation (CBSS) that exploits the sparsity of source signals in the frequency domain is addressed in this paper. We assume the sources follow complex Laplacian-like distribution for complex random variable, in which the real part and imaginary part of complex-valued source signals are not necessarily independent. Based on the maximum a posteriori (MAP) criterion, we propose a novel natural gradient method for complex sparse representation. Moreover, a new CBSS method is further developed based on complex sparse representation. The developed CBSS algorithm works in the frequency domain. Here, we assume that the source signals are sufficiently sparse in the frequency domain. If the sources are sufficiently sparse in the frequency domain and the filter length of mixing channels is relatively small and can be estimated, we can even achieve underdetermined CBSS. We illustrate the validity and performance of the proposed learning algorithm by several simulation examples.