The Iterated Extended Kalman Particle Filter for Speech Enhancement
Particle filters have been proposed as a new form of state-space filtering for speech enhancement applications.A crucial issue in particle filtering is the selection of the importance proposal distribution.In this paper,the iterated extended kalman filter (IEKF) is used to generate the proposal distribution.The proposal distribution integrates the latest measurements into state transition density,so it can match the posteriori density well.We apply time-varying autoregressive (TVAR) models with stochastically evolving parameters to the problem of speech modeling and enhancement,which is superior to conventional AR models.The experimental results indicate that the new particle filter superiors to the standard particle filter and the other filters such as the extended kalman particle filter (PF-EKF) in low SNR.
Speech enhancement Iterated extended kalman filter Particle filter Time-varying autoregressive models
XU Xin ZHAO Nan DONG Hang
Signal Processing Lab,School of Electronic Information Wuhan University,Wuhan,Hubei,430079,P.R.China
国际会议
9th International Conference on Signal Processing(第九届国际信号处理学术会议)(ICSP08)
北京
英文
2008-10-26（万方平台首次上网日期，不代表论文的发表时间）