基于极值域均值模式分解最大相似度的语音增强方法

提出一种基于极值域均值模式分解最大相似度的低信噪比语音增强算法，解决部分噪声环境下低信噪比语音信号增强问题。该算法核心思想是：对分解后得到的固有模态分量进行筛选后再做滤波处理，以此减少过滤波和欠滤波情况的发生。在筛选过程中，提出一种最大相似度判断算法，通过检测得到的噪声信号与固有模态分量计算最大相似度，通过最大相似度筛选出固有模态分量进行滤波，由于噪声与语音信号容易发生频谱混叠，在滤波器的选择上采用时域滤波器。将滤波后的固有模态分量和未作处理的固有模态分量进行信号重构，得到增强后结果。

Research on speech enhancement methods based on EMMD and maximum similarity

The paper presented a speech signal with low SNR algorithm based on EMMD for solving the speech signals enhancement with low SNR in any environments. The core of the algorithm is to filter after screening decomposited IMFs, preventing over-filter and owe-filter. The article presented a maximum sim- ilarity judgement algorithm by the maximum similarity of noise signals and IMFs. As noise and speech sig- nals easily spectrum aliasing, we adopted time domain filter by the maximum similarity screening IMFs. The new signal has been reconstructed by IMFs which has been filter and IMFs Which has been no process- ing. Then enhancement results have been obtained.