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A. Jukic, T. van Waterschoot, T. Gerkmann, and S. Doclo, "A General Framework for Multichannel Speech Dereverberation Exploiting Sparsity," Paper 9-1, (2016 January.). doi: A. Jukic, T. van Waterschoot, T. Gerkmann, and S. Doclo, "A General Framework for Multichannel Speech Dereverberation Exploiting Sparsity," Paper 9-1, (2016 January.). doi:
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.

@article{jukic2016a,
author={jukic, ante and van waterschoot, toon and gerkmann, timo and doclo, simon},
journal={journal of the audio engineering society},
title={a general framework for multichannel speech dereverberation exploiting sparsity},
year={2016},
volume={},
number={},
pages={},
doi={},
month={january},} @article{jukic2016a,
author={jukic, ante and van waterschoot, toon and gerkmann, timo and doclo, simon},
journal={journal of the audio engineering society},
title={a general framework for multichannel speech dereverberation exploiting sparsity},
year={2016},
volume={},
number={},
pages={},
doi={},
month={january},
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.},}

TY - paper
TI - A General Framework for Multichannel Speech Dereverberation Exploiting Sparsity
SP - EP -
AU - Jukic, Ante
AU - van Waterschoot, Toon
AU - Gerkmann, Timo
AU - Doclo, Simon
PY - 2016
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - January 2016 TY - paper
TI - A General Framework for Multichannel Speech Dereverberation Exploiting Sparsity
SP - EP -
AU - Jukic, Ante
AU - van Waterschoot, Toon
AU - Gerkmann, Timo
AU - Doclo, Simon
PY - 2016
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - January 2016
AB - 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.