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M. Hu, D. Sharma, S. Doclo, M. Brookes, and PA. A.. Naylor, "Blind Adaptive SIMO Acoustic System Identification Using a Locally Optimal Step-Size," Paper 6-2, (2016 January.). doi: M. Hu, D. Sharma, S. Doclo, M. Brookes, and PA. A.. Naylor, "Blind Adaptive SIMO Acoustic System Identification Using a Locally Optimal Step-Size," Paper 6-2, (2016 January.). doi:
Abstract: Blind adaptive identification of a Single-Input Multiple-Output (SIMO) acoustic system has useful applications including acoustic environment sensing, source localization and, in combination with multichannel equalization, dereverberation. An empirically chosen step-size is usually employed in blind system identification algorithms based on cross-relation error minimization. Although some adaptive step-size approaches have been proposed in the literature, the derivations rely, in some cases, on coarse approximations. In this paper, a locally optimal adaptive-step size exploiting the algebraic nature of the problem is derived. Experimental results using simulated room impulse responses show that the proposed algorithm has higher initial convergence rate.

@article{hu2016blind,
author={hu, mathieu and sharma, dushyant and doclo, simon and brookes, mike and naylor, patrick a.},
journal={journal of the audio engineering society},
title={blind adaptive simo acoustic system identification using a locally optimal step-size},
year={2016},
volume={},
number={},
pages={},
doi={},
month={january},} @article{hu2016blind,
author={hu, mathieu and sharma, dushyant and doclo, simon and brookes, mike and naylor, patrick a.},
journal={journal of the audio engineering society},
title={blind adaptive simo acoustic system identification using a locally optimal step-size},
year={2016},
volume={},
number={},
pages={},
doi={},
month={january},
abstract={blind adaptive identification of a single-input multiple-output (simo) acoustic system has useful applications including acoustic environment sensing, source localization and, in combination with multichannel equalization, dereverberation. an empirically chosen step-size is usually employed in blind system identification algorithms based on cross-relation error minimization. although some adaptive step-size approaches have been proposed in the literature, the derivations rely, in some cases, on coarse approximations. in this paper, a locally optimal adaptive-step size exploiting the algebraic nature of the problem is derived. experimental results using simulated room impulse responses show that the proposed algorithm has higher initial convergence rate.},}

TY - paper
TI - Blind Adaptive SIMO Acoustic System Identification Using a Locally Optimal Step-Size
SP - EP -
AU - Hu, Mathieu
AU - Sharma, Dushyant
AU - Doclo, Simon
AU - Brookes, Mike
AU - Naylor, Patrick A.
PY - 2016
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - January 2016 TY - paper
TI - Blind Adaptive SIMO Acoustic System Identification Using a Locally Optimal Step-Size
SP - EP -
AU - Hu, Mathieu
AU - Sharma, Dushyant
AU - Doclo, Simon
AU - Brookes, Mike
AU - Naylor, Patrick A.
PY - 2016
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - January 2016
AB - Blind adaptive identification of a Single-Input Multiple-Output (SIMO) acoustic system has useful applications including acoustic environment sensing, source localization and, in combination with multichannel equalization, dereverberation. An empirically chosen step-size is usually employed in blind system identification algorithms based on cross-relation error minimization. Although some adaptive step-size approaches have been proposed in the literature, the derivations rely, in some cases, on coarse approximations. In this paper, a locally optimal adaptive-step size exploiting the algebraic nature of the problem is derived. Experimental results using simulated room impulse responses show that the proposed algorithm has higher initial convergence rate.