Particle filtering for non-stationary speech modelling and enhancement
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 di usion processes. The main aim of the paper is to perform on-line estimation of the clean speech and the model parameters, and to determine the adequacy of the chosen statistical models. An eÆcient simulationbased method is developed to solve the optimal ltering problem. The algorithm combines sequential importance sampling and a selection step, and employs several variance reduction strategies to make the best use of the statistical structure of the model. The modelling and enhancement performance of the model and algorithm are evaluated in simulation studies on real speech data sets.