摘要
本文对经验模式分解(EMD)时频分析进行研究,具体的讲述了EMD算法相关基本概念以及EMD分解方法,同时分析了EMD的改进方法,提出了基于EMD与小波阈值滤波结合进行信号降噪的方法,根据这一方法对非平稳、非线性信号在高斯白噪声下进行了降噪,最后基于MATLAB仿真对小波阈值降噪、基于EMD的小波阈值降噪法进行了比较。仿真结果表明,后者效果好。
This article is researching for empirical mode decomposition （EMD） time-frequency analysis, and specific tells the related basic concepts of the decomposition method. In addition, an improved speech enhancement method based on empirical mode decomposition and wavelet threshold filtering, is put forward. As an example in this paper, an nonlinear, non - stationary signal is processed according to this method to get to noise cancellation in a white Gaussian noise channel. Finally it compares on wavelet de- noising and the EMD method of wavelet de-noising based on MATLAB simulation, the results show the latter get better effect.
出处
《网络安全技术与应用》
2013年第8期42-44,共3页
Network Security Technology & Application
基金
黑龙江省教育厅科学技术研究项目 项目编号:11553099
关键词
经验模式分解
小波阈值滤波
非线性信号
仿真
empirical mode decomposition
wavelet threshold filtering
an nonlinear signal
simulation
