“Bayesian single channel blind dereverberation of speech form a moving talker”
J. R. Hopgood, C. Evers , and S. Fortune
This chapter discusses a model-based framework for single-channel blind dereverberation of speech, in which parametric models are used to represent both the unknown source and the unknown acoustic channel. The parameters of the entire model are estimated using the Bayesian paradigm, and an estimate of the source signal is found by either inverse filtering of the observed signal with the estimated channel coefficients, or directly within a sequential framework. Model-based approaches fundamentally rely on the availability of realistic and tractable models that reflect the underlying speech process and acoustic systems. The choice of these models is extremely important and is discussed in detail, with a focus on spatially varying room impulse responses. The mathematical framework and methodology for parameter estimation and dereverberation is also discussed. Some examples of the proposed approaches are presented with results.