Parametric modelling for single-channel blind dereverberation of speech from a moving speaker
Christine Evers, James R. Hopgood · IET Signal Processing · 2008
Single-channel blind dereverberation for the enhancement of speech acquired in acoustic environments is essential in applications where microphone arrays prove impractical. In many scenarios, the source–sensor geometry is not varying rapidly, but in most applications the geometry is subject to change, for example when a user wishes to move around a room. A previous model-based approach to blind dereverberation by representing the channel as a linear time-varying all-pole filter is extended, in which the parameters of the filter are modelled as a linear combination of known basis functions with unknown weightings. Moreover, an improved block-based time-varying autoregressive model is proposed for the speech signal, which aims to reflect the underlying signal statistics more accurately on both a local and global level. Given these parametric models, their coefficients are estimated using Bayesian inference, so that the channel estimate can then be used for dereverberation. An in-depth discussion is also presented about the applicability of these models to real speech and a real acoustic environment. Results are presented to demonstrate the performance of the Bayesian inference algorithms.