摘要
基于最大熵法(Maxim um Entropy, ME)、最小互信息量法(Minim um Mutual Inform a-tion, MMI)和最大似然法(Maxim um Likelihood, ML)是解决盲信号分离问题的常用算法,分析了ME、MMI以及ML算法之间关系.基于高斯混合模式(Gaussian Mixture Model, GMM)概率密度函数估计,提出了一种采用反馈结构的扩展最大熵语音分离算法.与传统ME的计算机模拟实验结果比较得知,新算法具有更好的收敛性能和语音分离效果.
The speech separation task was seen as a convolution mixture blind signal separation (BSS) problem. There are 3 kinds of main approaches to solve the BSS problem: ME(maximum entropy) algorithm, MMI(minimum mutual information) algorithm, and ML (maximum likelihood) algorithm. The relationship among the 3 kinds of algorithms was analyzed in this paper. Based on the feedback architecture and Gaussian mixture model (GMM) probability density function (pdf) estimation, a new extended ME algorithm speech separation algorithm was proposed. Based on the computer simulations of the proposed algorithm and traditional ME algorithm, it can be concluded that the proposed algorithm has better convergence performance.
出处
《上海交通大学学报》
EI
CAS
CSCD
北大核心
2000年第2期177-180,共4页
Journal of Shanghai Jiaotong University
基金
国家自然科学基金!(69672007)
关键词
语音分离
盲信号分离
高斯混合模式
密度函数
speech separation
blind signal separation (BSS)
Gaussian mixture model (GMM)
feedback architecture