We consider the problem of blind multi-channel speech dereverberation without the knowledge of room acoustics. The dereverberated speech component is estimated by subtracting the undesired component, estimated using multi-channel linear prediction (MCLP), from the reference microphone signal. In this paper we present a framework for MCLP-based speech dereverberation by exploiting sparsity in the time-frequency domain. The presented framework uses a wideband or a narrowband signal model and a sparse analysis or synthesis model for the desired speech component. The proposed problems involving a reweighted $\ell_1$-norm, are solved in a flexible optimization framework. The obtained results are comparable to the state of the art, motivating further extensions exploiting sparsity and speech structure.

Authors: Jukic, Ante; van Waterschoot, Toon; Gerkmann, Timo; Doclo, Simon
Affiliations: KU Leuven, Leuven, Belgium; University of Oldenburg, Oldenburg, Germany(See document for exact affiliation information.)
AES Conference: 60th International Conference: DREAMS (Dereverberation and Reverberation of Audio, Music, and Speech) (January 2016)
Paper Number: 9-1
Publication Date: January 27, 2016
Subject: Paper Session 9

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