关键词: 盲源分离, 独立分量分析, 主成分分析

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