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A new modified least mean square Newton (LMS-Newton) algorithm improves active noise control by reducing computational complexity. This method enhances noise reduction performance and convergence speed, even with multiple reference signals.

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Area of Science:

  • Engineering
  • Signal Processing
  • Acoustics

Background:

  • Feedforward active noise control (ANC) systems often require more reference signals for better performance.
  • Increasing reference channels in ANC typically degrades adaptive algorithm convergence speed and increases computational load.

Purpose of the Study:

  • To propose a modified least mean square Newton (LMS-Newton) algorithm for feedforward ANC.
  • To reduce computational complexity while maintaining convergence performance in multi-channel ANC systems.

Main Methods:

  • A modified LMS-Newton algorithm utilizing block coordinate descent is introduced.
  • The control filter is divided into channel-wise blocks, with sequential updates for each block.
  • Theoretical analysis confirms convergence to the Wiener solution with reliable correlation function estimation.

Main Results:

  • The proposed algorithm achieves significant noise reduction (11.1 dBA left ear, 9.9 dBA right ear) within 40 seconds using 42 reference signals.
  • It demonstrates reduced convergence time compared to the filtered-x normalized least mean square (FxNLMS) algorithm.
  • Achieved a 74% reduction in computational complexity versus FxNLMS and a 98% reduction versus the standard LMS-Newton algorithm.

Conclusions:

  • The modified LMS-Newton algorithm offers an efficient solution for multi-channel active noise control.
  • It effectively balances noise reduction performance, convergence speed, and computational complexity.
  • This approach is validated by simulations using real-world road noise data.