基于改进CEEMDAN和小波阈值的雨声信号去噪算法研究

为了从混杂着各种噪声的雨声信号中提取到较为纯净的雨声信号，本文提出基于改进完全自适应噪声集合经验模态分解（CEEMDAN）和小波阈值相结合的雨声信号去噪方法。方法引入互相关函数寻找CEEMDAN的最优分解层数F值，并通过CEEMDAN算法按最优分解层数F层分解，将信号分解成多个频率由高到低的本征模态分量（IMF）;利用小波阈值，滤除高频IMF分量中的噪声分量，最后将去噪后的高频IMF分量和未经去噪的低频IMF分量进行信号重构，提取出较为纯净的雨声信号；实验表明，本文选用方法的去噪效果相对于经验模态分解（EMD）去噪算法、小波阈值去噪算法等传统方法具有一定的优势，去噪后的雨声信号能够准确反映出环境雨情的特征，提高雨情分析的精确度。

Research on denoising algorithm of rain signal based on improved CEEMDAN and wavelet threshold

In order to extract the purer rain sound signal from the rain sound signal mixed with various noises, this paper proposes a denoising method of rain sound signal based on the combination of improved fully adaptive noise set empirical mode decomposition (CEEMDAN) and wavelet threshold. Methods Cross correlation function was introduced to find the optimal decomposition level F value of CEEMDAN, and the signal was decomposed into multiple intrinsic mode components (IMF) with high frequency to low frequency by CEEMDAN algorithm. Using wavelet threshold, the noise component in the high frequency IMF component is filtered out, and finally, the denoised high frequency IMF component and the denoised low frequency IMF component are reconstructed to extract a relatively pure rain sound signal. The experiment shows that the denoising effect of this method is superior to the traditional methods such as empirical mode decomposition (EMD) denoising algorithm and wavelet threshold denoising algorithm, and the denoised rain signal can accurately reflect the characteristics of environmental rain, thus improving the accuracy of rain analysis.