Xie, JingliJin, DanqiZhang, WenZhang, Xiao-LeiChen, JieWang, DeLiang2020-09-22May 12-17978-1-5386-4658-8http://hdl.handle.net/1885/211357Multichannel active noise control (MC-ANC) aims to cancel low-frequency noise in an enclosure. If noise sources are distributed sparsely in space, adding an `1-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 `1-norm constrained MCANC (c`1-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 c`1-MC-ANC, the proposed algorithms exhibit faster convergence or higher noise reduction at steady state in both free field and reverberant environmentsThis work was supported in part by the National Natural Science Foundation of China (NSFC) funding scheme under Project No. 61671380, No. 61671381 and No. 61671382.application/pdfen-AU©2019 IEEE201910.1109/ICASSP.2019.86831572020-06-23