Model-based Analysis-by-Synthesis for the Dereverberation of Speech and Audio Signals

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This project deals with the dereverberation of speech signals by means of multichannel blind channel identification and equalization. This final proposal aims at a systematic enhancement of the current approach by using statistical methods. The upcoming optimization will be based on Bayesian inference principles that extend the existing Maximum Likelihood (ML) solution and a Maximum A Posterior (MAP) estimator towards a Variational Bayes (VB) estimation algorithm.Strong models for the unknown acoustic system and the source signal allow for the derivation of powerful VB algorithms. Based upon the existing Markov model for the channels, this last project phase will particularly address the modeling of the source signal. By introducing a priori information about the unknown quantity, the resulting algorithms will then be specifically tailored to the dereverberation problem in order to mitigate remaining estimation errors. The optimization in a VB sense should eventually lead to an efficient estimation algorithm that adaptively estimates the channel and the source signal as well as the corresponding model parameters. Such a systematic enhancement of the proposed system from ML to MAP and VB moreover allows for a structural comparison of all three estimation approaches and an analysis of their individual strengths and shortcomings. All this will help to assess the overall benefit of the proposed methods.