Robust Sparse Multichannel Active Noise Control
Jingli Xie, Danqi Jin, Wen Zhang, Xiao-Lei Zhang, Jie Chen, DeLiang Wang
Abstract
Multichannel active noise control (MC-ANC) aims to cancel low-frequency noise in an enclosure. If noise sources are distributed sparsely in space, adding an ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -norm constraint to the standard MC-ANC helps to reduce the complexity of the system and accelerate the convergence rate. However, the convergence performance of ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -norm constrained MC-ANC (cℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -MC-ANC) degrades significantly in reverberant environments. In this paper, we analyze the necessity of using sparsity-inducing algorithms with distinct zero-attracting strengths over loudspeakers, and then derive three algorithms of this kind in the complex domain. Simulation results show that, compared to cℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -MC-ANC, the proposed algorithms exhibit faster convergence or higher noise reduction at steady state in both free field and reverberant environments.
BibTeX
@inproceedings{icassp2019_robustsparsemult,
title = {Robust Sparse Multichannel Active Noise Control},
author = {Jingli Xie and Danqi Jin and Wen Zhang and Xiao-Lei Zhang and Jie Chen and DeLiang Wang},
booktitle = {ICASSP 2019},
year = {2019}
}