哈尔滨工业大学学报（英文版）2007，Vol.14Issue(3)：362-367,6.
Blind source separation based on generalized gaussian model
Blind source separation based on generalized gaussian model
摘要
Abstract
Since in most blind source separation (BSS) algorithms the estimations of probability density function (pdf) of sources are fixed or can only switch between one sup-Gaussian and other sub-Gaussian model,they may not be efficient to separate sources with different distributions. So to solve the problem of pdf mismatch and the separation of hybrid mixture in BSS, the generalized Gaussian model (GGM) is introduced to model the pdf of the sources since it can provide a general structure of univariate distributions. Its great advantage is that only one parameter needs to be determined in modeling the pdf of different sources, so it is less complex than Gaussian mixture model. By using maximum likelihood (ML) approach, the convergence of the proposed algorithm is improved. The computer simulations show that it is more efficient and valid than conventional methods with fixed pdf estimation.关键词
blind source separation/Independent Component Analysis/Generalized Gaussian Model/Maximum LikelihoodKey words
blind source separation/Independent Component Analysis/Generalized Gaussian Model/Maximum Likelihood分类
信息技术与安全科学引用本文复制引用
YANG Bin,KONG Wei,ZHOU Yue..Blind source separation based on generalized gaussian model[J].哈尔滨工业大学学报（英文版）,2007,14(3):362-367,6.基金项目
Sponsored by the Foundation of CSSC ( Grant No. 03J3.4.3) and the 863 Hi-tech Research and Development Program of China ( No. 2006AA09Z210). （ Grant No. 03J3.4.3）
