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。
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 simi- larity 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.
出处
《计算机工程与应用》
CSCD
2012年第10期124-127,共4页
Computer Engineering and Applications
基金
国家自然科学基金(No.61075008)
关键词
盲源分离
独立分量分析
主成分分析
blind source separation
independent component analysis
principle component analysis
