Channel self-adjusting filtered-x LMS algorithm for active control of vehicle road noise

Tao Feng, Guohua Sun, Mingfeng Li, Teik C. Lim

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Current active road noise control (ARNC) systems, configured with the standard filtered-x least mean square (FxLMS) algorithm, are not sufficient enough to yield an ideal noise reduction over a broad frequency range. This is because ARNC systems generally employ multiple reference signals, which has an inherent limitation of the channel-dependent convergence behaviour due to dynamic characteristics amongst reference signals. In this study, an effective ARNC system with the channel self-adjusting FxLMS (CSFxLMS) algorithm by incorporating the self-adjusting parameter on each reference signal path is proposed, which is to minimise the effect of its dynamics characteristics in different reference channels. To validate the effectiveness of the proposed algorithm, numerical simulations using measured road noise response is conducted. Results show that the performance of the proposed CSFxLMS algorithm is significantly better as compared to the conventional FxLMS algorithm, and is able to achieve around 5 dBA reductions at the driver's ear position.

Original languageEnglish (US)
Pages (from-to)267-281
Number of pages15
JournalInternational Journal of Vehicle Noise and Vibration
Issue number3-4
StatePublished - 2017
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering


  • ANC
  • Active noise control
  • Convex combination method
  • Filtered-x LMS algorithm
  • Road noise


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