Lian Siyuan, Li Tianyou, Gu Jincheng, Hu Yuxiang, Zhu Changbao, Wang Shuping, Lu Jing
Key Laboratory of Modern Acoustics, Institute of Acoustics, Nanjing University, Nanjing 210093, China.
NJU-Horizon Intelligent Audio Lab, Nanjing Institute of Advanced Artificial Intelligence, Nanjing 210014, China.
J Acoust Soc Am. 2024 Aug 1;156(2):1413-1424. doi: 10.1121/10.0028312.
Active road noise control (ARNC) systems have been widely investigated for low-frequency road noise attenuation in vehicle cabins. Multiple reference and error sensors are usually required to ensure noticeable noise reduction. However, this tends to slow down the convergence speed of adaptive algorithms due to the coupling of secondary paths and the cross correlation of reference signals. Furthermore, the high computational burden of normally utilized multichannel control algorithms exacerbates the difficulty of practical implementations. In this paper, an online decoupling-whitening frequency domain filtered-error least mean square (ODW-FDFeLMS) algorithm is proposed to address the aforementioned problems. Secondary path decoupling through inner-outer product decomposition and online reference whitening through spectral factorization effectively accelerate the convergence rate. Additionally, the utilization of the filtered-error algorithm based on frequency domain processing mitigates the computational complexity. Simulations with measured road noise data confirm the superiority of the ODW-FDFeLMS algorithm over existing algorithms in terms of convergence speed and computational complexity. Real-time experiments in a vehicle cabin further confirm the effectiveness of the proposed algorithm.
有源道路噪声控制(ARNC)系统已被广泛研究用于降低车辆驾驶室内的低频道路噪声。通常需要多个参考传感器和误差传感器来确保显著的降噪效果。然而,由于次级路径的耦合和参考信号的互相关性,这往往会减慢自适应算法的收敛速度。此外,通常使用的多通道控制算法的高计算负担加剧了实际实现的难度。本文提出了一种在线解耦白化频域滤波误差最小均方(ODW-FDFeLMS)算法来解决上述问题。通过内外积分解进行次级路径解耦以及通过谱分解进行在线参考白化有效地加快了收敛速度。此外,基于频域处理的滤波误差算法的使用降低了计算复杂度。利用实测道路噪声数据进行的仿真证实了ODW-FDFeLMS算法在收敛速度和计算复杂度方面优于现有算法。在车辆驾驶室内进行的实时实验进一步证实了所提算法的有效性。