改进的非负矩阵分解语音增强算法  

Improved Nonnegative Matrix Factorization based Speech Enhancement Algorithm

作　　者：胡永刚[1] 张雄伟[1] 邹霞[1] 闵刚[1,2] 郑云飞[1] 李莉[1] 石佳佳[3] 

机构地区：[1]解放军理工大学指挥信息系统学院,南京210007 [2]西安通信学院,西安710000 [3]华东师范大学数学系,上海201100

出　　处：《信号处理》2015年第9期1117-1123,共7页Journal of Signal Processing

基　　金：国家自然科学基金资助项目(61471394;61402519);江苏省自然科学青年基金(BK20140071;BK20140074)

摘　　要：本文提出了一种改进的非负矩阵分解语音增强算法,该算法可分为训练和增强两部分。首先,为了降低训练复杂度,采用卷积非负矩阵分解只提取噪声字典。增强时,考虑语音信号稀疏性比噪声信号稀疏性强,通过稀疏非负矩阵分解重构出语音幅度谱,采用交替方向乘子法进行优化迭代,克服了经典乘性迭代易陷入局部最优、分母只能收敛到零极限等问题。最后,基于算法融合的思想,将重构的语音幅度谱与谱减法、最小均方误差幅度谱估计得到的幅度谱进行加权融合。仿真实验中,在10种不同噪声环境中,通过多种评价标准证明所提算法能取得较好的增强效果。This paper proposes an improved Nonnegative Matrix Factorization based speech enhancement algorithm. It mainly consists of a training stage and an enhancement stage. Firstly,to reduce the complexity in the training stage,only noise dictionary is trained using Convolution Nonnegative Matrix Factorization as the prior information. During the enhancement stage,considering the different sparse properties shown between noise and speech,the speech is separated from noise by using Sparse Nonnegative Matrix Factorization. Particularly,the update process is solved by putting the theory of Alternating Direction Method of Multipliers,which can solve the problems such as poor local optima and limitation of convergence to zero in the traditional speech enhancement using Nonnegative Matrix Factorization. Finally,to take advantage of the unsupervised speech enhancement algorithm,Spectral Subtraction and Minimum Mean Square Log-spectral based methods are combined to improve the performance. Extensive experiments indicate that the proposed algorithm shows promising noise suppression ability under ten various noise conditions.

关 键 词：稀疏非负矩阵分解 交替方向乘子法 算法融合 

分 类 号：TN912.3[电子电信—通信与信息系统]