Active Noise Control based on the Momentum Multichannel Normalized Filtered-x Least Mean Square Algorithm

Multichannel active noise control (MCANC) is widely utilized to achieve more significant noise cancellation area in the complicated acoustic field. Meanwhile, the filter-x least mean square (FxLMS) algorithm gradually becomes the benchmark solution for the implementation of MCANC due to its low computational complexity. However, its slow convergence speed more or less undermines the performance of dealing with quickly varied disturbances, such as vehicle noise. Furthermore, the variation of the noise power also deteriorates the robustness of the algorithm when it adopts the fixed step size. To solve these issues, we integrated the normalized multichannel FxLMS with the moment method, which effectively avoids the interference of the primary noise power and accelerates the convergence of the algorithm. To validate its effectiveness, we deployed this algorithm in a multichannel noise control window to control the real tra c noise.