小波去噪算法在含噪盲源分离中的应用

无噪模型下的盲源分离算法在信噪比较低的情况下并不适用。针对该情况一种解决方案就是先对含有高斯白噪声的混合信号进行去噪预处理，然后使用盲源分离算法进行分离。为此，本文提出了一种适用于信噪比较低条件下的基于平移不变量的小波去噪算法。该算法首先使用高频系数滑动窗口法准确估计含噪混合信号的噪声方差，然后使用Bayesshrink阈值估计算法 得到更加合理的阈值，最后在不降低去噪效果的同时缩小了平移不变量的范围，减少了运算量。实验仿真表明，在信噪比较低的情况下，与传统小波去噪算法相比，该算法可以更加有效地去除噪声，在很大程度上提升盲源分离算法的性能。

Application of Wavelet Denoising Algorithm in Noisy Blind Source Separation

Blind source separation (BSS) algorithms based on the noise free model are not applicable when the SNR is low. To deal with this issue, one way is to denoise the mixtures corrupted by white Gaussian noise, firstly, and then utilize the BSS algorithms. Therefore, a Waveshrink algorithm is proposed based on translation invariant to denoise mixtures with strong noise. The high frequency coefficients sliding window method is utilized to estimate the noise variance accurately, and Bayesshrink algorithm is utilized for a more reasonable threshold. Consequently, the scope of the translation invariant is narrowed without degrading the performance of denoising, thus reducing the computation amount. Simulation results indicate that the proposed approach perform better in denoising compared with the traditional Waveshrink algorithm, and can remarkably enhance the separation performance of BSS algorithms, especially in the case with low signa.