High Dimension Finite Mixture Gaussian Model Estimation for Short Time Fourier Decomposition by EM-Algorithm
A modi .cation of the central limit theorem indi- cates that for a stationary or asymptotically stationary ran- dom process,its Fourier coef .cients are independent complex Gaussian random variables 1 .We apply this idea in the short time Fourier transform,where most process has the asymp- totic stationary property in short time sense.The estimated parameters of the complex Gaussian distribution can be used in the feature extraction or the plug-in hypothesis test for recognition.The problem becomes to estimate the parameters of the complex Gaussian.The windowed short time Fourier coef .cients are not simple complex Gaussian but contaminated Gaussian,which means we need to estimate the parameters of mixture Gaussian.The EM-algorithm could estimate the parameters directly but the M-step is still complicate.Re- casting the contaminated Gaussian as a .nite mixture Gaussian model,we can estimated the mean vector and covariance matrix for each time-frequency bin.Estimate the parameters of a mixture high-dimension joint Gaussian distribution with high accuracy and low computation cost shows a good way to solve the problem of distribution estimation.With the estimated distribution,we can create a statistical model for recognition. This method is examined with a mixture 2 dimension joint Gaussian distribution and the simulation results are discussed with good performance.The convergence preserved by the EM- algorithm and the convergence rate is examined too.
short-time Fourier transform distribution es- timation EM-Algorithm Finite Mixture Model
Mei Chen Yan Liu Mingguang Zhuang
Department of Electrical and Systems Engineering Washington University in St.Louis Saint Louis,Misso School of Software Engineering Tongji University Shanghai 200092,China
国际会议
2008 IEEE International Conference on Onformation and Automation(IEEE 信息与自动化国际会议)
张家界
英文
686-691
2008-06-20（万方平台首次上网日期，不代表论文的发表时间）