Abstract: In order to solve the slow convergence problem of ICA based algorithm and high computational cost due to excessive amount data, an blind separation algorithm based on PCA-ICA for speech signal is proposed. PCA is used to remove the second-order correlations among different dimensions of feature from original data. Using similarity coefficient matrix as the separation effect standard, the simulation experiment results show that the proposed method can reduce 90% of iterations and is 3 times faster compared with ICA with the same separation accuracy. Thus the ICA-PCA algorithm effectively solves the slow convergence problem of original ICA method.

Key words: blind source separation, independent component analysis, principle component analysis

摘要： 针对ICA用于语音信号盲分离时，由于数据量过大、迭代次数过多引起的收敛速度慢的问题，采用一种PCA和ICA相结合的盲分离算法PCA-ICA。通过PCA对混合语音信号进行白化处理，消除了原始各道数据间的二阶相关性。在仿真实验中，采用相似系数矩阵作为评价混合语音信号分离效果的标准，结果表明PCA-ICA算法与ICA算法相比，在达到几乎相同的相似系数矩阵的情况下，迭代次数减少了90%，从而分离速度提高了3倍，有效地解决了ICA分离算法收敛速度慢的问题。

关键词: 盲源分离, 独立分量分析, 主成分分析