基于ICA算法的集合经验模态分解去噪方法
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摘要
针对非线性非平稳信号的去噪问题,提出了一种基于独立分量分析(Decomposition Components Analysis,简称ICA)算法的集合经验模态分解去噪方法。首先利用白噪声辅助数据分析方法——集合经验模态分解(Ensemble Empirical Mode Decomposition,简称EEMD)有效的抑制了经验模态分解(Empirical Mode Decomposition,简称EMD)中存在的端点效应和模态混叠现象,然后利用ICA算法对含噪信号经过EEMD分解后的有限个固有模态函数(Intrinsic Mode Function,简称IMF)进行去噪处理,有效的分离出若干个有效的语音信号分量,并对其进行语音重构,最后与小波阈值去噪方法进行比较,通过仿真可以看出,该方法对于信号去噪较为理想。
In order to focus on the denoise of Nonlinear nonstationary signals, we first use the white noise assisted data analysis called EEMD(ensemble empirical mode decomposition) based on the ICA(decomposition Components Analysis) to suppress the end effect and effect and aliasing which appear in the empirical mode decomposition in the process of empirical mode decomposition(EMD). And then, we use ICA to denoise the noise signals which have been decomposed by EEMD into IMF, and we can efficiently decompose some effective speech signals components and reconstruct them. Finally, compared with the waved threshold method, the effect of this solution is ideal.
引文
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