Speech Signal Enhancement Based on MAP Algorithm in the ICA Space
Speech Signal Enhancement Based on MAP Algorithm in the ICA Space
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DOI：
10.1109/tsp.2007.910555
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发表时间：
2008-05
影响因子：
5.4
通讯作者：
Xin Zou;P. Jančovič;Ju Liu;M. Köküer
中科院分区：
文献类型：
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作者：
Xin Zou;P. Jančovič;Ju Liu;M. Köküer
提出了一种基于独立分量分析（伊卡）的最大后验（MAP）去噪算法。我们证明，就业的信号和噪声的个别伊卡变换可以提供最好的估计内的线性框架。根据信号和噪声的分布是高斯分布还是非高斯分布，对信号增强问题进行了分类，并推导了每类问题的估计规则。我们的理论分析表明，在高斯噪声的假设下，所提出的算法导致一些众所周知的增强技术，即，维纳滤波器和稀疏码收缩。对该算法的去噪性能分析表明，该算法对被非高斯噪声污染的非高斯信号是最有效的。我们采用广义高斯模型（GGM）来模拟语音和噪声的分布。实验评估进行的信号噪声比（SNR）和频谱失真的措施。实验结果表明，该算法在高斯噪声和非高斯噪声下的增强性能都有显著提高。
This paper presents a novel maximum a posteriori (MAP) denoising algorithm based on the independent component analysis (ICA). We demonstrate that the employment of individual ICA transformations for signal and noise can provide the best estimate within the linear framework. The signal enhancement problem is categorized based on the distribution of signal and noise being Gaussian or non-Gaussian and the estimation rule is derived for each of the categories. Our theoretical analysis shows that under the assumption of a Gaussian noise the proposed algorithm leads to some well-known enhancement techniques, i.e., Wiener filter and sparse code shrinkage. The analysis of the denoising capability shows that the proposed algorithm is most efficient for non-Gaussian signals corrupted by a non-Gaussian noise. We employed the generalized Gaussian model (GGM) to model the distributions of speech and noise. Experimental evaluation is performed in terms of signal-to-noise ratio (SNR) and spectral distortion measure. Experimental results show that the proposed algorithms achieve significant improvement on the enhancement performance in both Gaussian and non-Gaussian noise.