@inproceedings {Jukic1556_2016,
	year = {2016},
	author = {Jukic, Ante and van Waterschoot, Toon and Gerkmann, Timo and Doclo, Simon},
	title = {A General Framework for Multichannel Speech Dereverberation Exploiting Sparsity},
	booktitle = {AES Conference: 60th International Conference (DREAMS)},
	publisher = {AES E-Library},
	number = {Session 9},
	URL = {http://www.aes.org/e-lib/browse.cfm?elib=18089 },
	abstract = {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.}
}