@article {Long:2018:0736-2935:4776,
title = "Multi-Channel Adaptive Feedforward Systems for Multi-Input Multi-Output Active Control of Broadband Road Noise",
journal = "INTER-NOISE and NOISE-CON Congress and Conference Proceedings",
parent_itemid = "infobike://ince/incecp",
publishercode ="ince",
year = "2018",
volume = "258",
number = "3",
publication date ="2018-12-18T00:00:00",
pages = "4776-4784",
itemtype = "ARTICLE",
issn = "0736-2935",
url = "https://ince.publisher.ingentaconnect.com/content/ince/incecp/2018/00000258/00000003/art00010",
author = "Long, Guo and Feng, Tao and Dhakad, Rushikesh and Lim, Teik",
abstract = "Multi-input, multi-output (MIMO) active noise control (ANC) has a wide variety of applications currently for low-frequency vehicle interior noise control configured with the standard filtered-x least mean squares (FxLMS) algorithm. However, the conventional broadband active control
of vehicle road noise using multiple reference accelerometers, multiple control speakers and multiple error sensors is often limited in practice by slow convergence speeds, high computational demand and poor overall noise reductions. In a simple context of a given multi-channel ANC system,
this paper presents two MIMO ANC models for vehicle road noise control based on the FxLMS algorithm and the delayless subband FxLMS (SFxLMS) algorithm, respectively. A comparison of the computational cost of the two MIMO ANC systems reveals that the SFxLMS-based system has potentially much
lower computational demand compared with the FxLMS-based one as the number of the subbands employed in the subband algorithm increases. Numerical simulations using actual data measured in a vehicle demonstrate that the MIMO SFxLMS algorithm achieves improved convergence and better overall
noise cancellation with significantly reduced computational cost as compared to the MIMO FxLMS algorithm.",
}