|
Abstract : |
A. Doucet is supported by EPSRC, UK. This paper applies time-varying autoregressive (TVAR) models with stochastically evolving parameters to the problem of speech modelling and enhancement. The stochastic evolution models for the TVAR parameters are Markovian diusion processes. The main aim of the paper is to perform on-line estimation of the clean speech and model parameters, and to determine the adequacy of the chosen statistical models. Ecient simulation-based methods are developed to solve the optimal ltering and xed-lag smoothing problems. The algorithms combine sequential importance sampling (SIS), a selection step and Markov chain Monte Carlo (MCMC) methods. They employ of several variance reduction strategies to make the best use of the statistical structure of the model. It is also shown how model adequacy may be determined by combining the simulation-based optimal lter with frequentist methods. The modelling and enhancement performance of the models and estimation algorithms are evaluated in simulation studies on both synthetic and real speech data sets. 1, |
