论文标题
基于深度学习的主动噪声控制的混合sfanc-fxnlms算法
A Hybrid SFANC-FxNLMS Algorithm for Active Noise Control based on Deep Learning
论文作者
论文摘要
选择性固定滤波器主动噪声控制（SFANC）方法为各种类型的噪声选择最佳的预训练的控制过滤器可以达到快速响应时间。但是，由于滤波器的选择不正确和缺乏适应性，可能导致稳态错误。相比之下，过滤的X归一化最小均值（FXNLMS）算法可以通过自适应优化获得较低的稳态误差。尽管如此，其缓慢的收敛对动态噪声衰减产生了不利影响。因此，本文提出了一种混合SFANC-FXNLMS方法来克服自适应算法的缓慢收敛，并提供比SFANC方法更好的降噪水平。轻量级的一维卷积神经网络（1D CNN）旨在自动为主噪声的每个框架选择最合适的预训练的控制滤波器。同时，FXNLMS算法继续以采样速率更新所选的预训练对照滤波器的系数。由于两种算法的有效组合，实验结果表明，混合SFANC-FXNLMS算法可以达到快速响应时间，低噪声误差和高度的鲁棒性。
The selective fixed-filter active noise control (SFANC) method selecting the best pre-trained control filters for various types of noise can achieve a fast response time. However, it may lead to large steady-state errors due to inaccurate filter selection and the lack of adaptability. In comparison, the filtered-X normalized least-mean-square (FxNLMS) algorithm can obtain lower steady-state errors through adaptive optimization. Nonetheless, its slow convergence has a detrimental effect on dynamic noise attenuation. Therefore, this paper proposes a hybrid SFANC-FxNLMS approach to overcome the adaptive algorithm's slow convergence and provide a better noise reduction level than the SFANC method. A lightweight one-dimensional convolutional neural network (1D CNN) is designed to automatically select the most suitable pre-trained control filter for each frame of the primary noise. Meanwhile, the FxNLMS algorithm continues to update the coefficients of the chosen pre-trained control filter at the sampling rate. Owing to the effective combination of the two algorithms, experimental results show that the hybrid SFANC-FxNLMS algorithm can achieve a rapid response time, a low noise reduction error, and a high degree of robustness.
