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.
翻译:选择性的固定过滤器活性噪音控制(SFANC)方法为各种类型的噪音选择最佳的预先训练控制过滤器(SFANC)可以实现快速反应时间。然而,由于过滤器选择不准确和缺乏适应性,这可能导致大量稳定状态错误。相比之下,过滤器X(FxNLMS)的常规最小平方形(FxNLMS)算法可以通过适应性优化获得较低的稳定状态错误。然而,它的缓慢趋同对动态噪音降温产生有害影响。因此,本文件建议采用混合的SFANC-FxNLMS 方法,以克服适应性算法的缓慢趋同,并提供比SFANC方法更好的降低噪音水平。一个轻量的单维电动神经网络(1D CNN)的设计是为了自动选择最合适的初级噪音框架的预先训练控制过滤器。与此同时,FXNLMS算法继续以抽样率更新所选择的预先训练控制过滤器的系数。由于两种算法的有效结合,实验结果显示,混合的SFANC-FXNMS的高度噪动性能能能能能能的快速降低度可以迅速降低。