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A neural reference projection-based method for multi-reference active noise control (L).

Yiming He¹, Lu Bai¹, Li Rao¹

¹Key Laboratory of Modern Acoustics, Institute of Acoustics, Nanjing University, Nanjing 210093, China.

The Journal of the Acoustical Society of America

|May 20, 2026

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A new neural method enhances active noise control (ANC) by compressing multichannel reference signals. This approach improves convergence speed and broadband noise reduction in vehicles.

Area of Science:

Engineering
Signal Processing
Machine Learning

Background:

Active noise control (ANC) systems require efficient processing of multichannel reference signals.
Conventional methods for reference signal compression can limit performance in complex acoustic environments.

Purpose of the Study:

To propose a novel neural reference projection-based method for compressing multichannel reference signals in feedforward ANC.
To integrate this method with the filtered-x affine projection algorithm to enhance ANC performance.

Main Methods:

A neural reference projection technique was developed for signal compression.
The proposed method was combined with the filtered-x affine projection algorithm.
Simulations were conducted using a real-vehicle road noise dataset.

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Main Results:

The neural reference projection-based filtered-x affine projection algorithm demonstrated faster convergence compared to conventional methods.
Higher broadband noise attenuation was achieved in multi-reference ANC scenarios.
The proposed method effectively improved both convergence speed and noise reduction performance.

Conclusions:

The neural reference projection method offers a significant advancement in feedforward ANC.
This technique provides superior performance for real-world applications like in-vehicle noise reduction.
The integration with filtered-x affine projection enhances the efficiency and effectiveness of ANC systems.