一种小波域盲源分离算法

针对基于相合束广义特征分解时域盲源分离方法受滤波器或时延影响大、性能不稳定的问题，提出了一种基于相合束广义特征分解的小波域盲源分离算法．该算法通过信号小波变换的正交性，增强信号的非高斯性，减小信号的分离难度;利用双正交小波具有线性相位特性且对信号有良好逼近能力的优点，对小波系数进行相合束广义特征分解，得到稳定的分离矩阵．该算法不仅保留了时域算法的优点，而且可以随机选取滤波器，当源信号多于3个时仍可以完全分离出源信号．4个语音信号的盲源分离仿真结果表明了算法的有效性．

Blind source separation method in the wavelet domain

Because the performance of the blind source separation method based on generalized eigendecomposition for congruent pencils in the time domain is affected by the filter or delay, a blind source separation method based on generalized eigendecomposition for congruent pencils in the wavelet domain is proposed. Using the orthogonal property of wavelet, the non-gaussianity of the signals is increased and the difficulty in separating signals is decreased. Since the bi-orthogonal wavelet has the linear phase property and good approximation ability to signals, the generalized eigendecomposition for congruent pencils of wavelet coefficients is carried out, and the steady separation matrix is obtained. The algorithm not only retains the virtue of the time domain algorithm, but also can use two random numbers as the filter coefficients, and the time delay method used by it can separate the mixtures when the number of the sources is more than three. The simulation results of four voice signals verify the effectiveness of this method.