Blind speech dereverberation using batch and sequential Monte Carlo methods

Reverberation and noise cause significant deterioration of audio quality and intelligibility to signals recorded in acoustic environments. Bayesian dereverberation infers knowledge about the system by exploiting the statistical properties of speech and the acoustic channel. In Bayesian frameworks, the signal can be processed either sequentially using online methods or in a batch using offline methods. This paper compares the two approaches for blind speech dereverberation by means of a previously proposed batch approach and a novel sequential approach. Results show that while both methods have different advantages, online processing leads to a more flexible solution.