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Active noise control (ANC) systems can efficiently attenuate low-frequencynoises by introducing anti-noises to combine with the unwanted noises. In ANCsystems, the filtered-x least mean square (FxLMS) and filtered-X normalizedleast-mean-square (FxNLMS) algorithm are well-known algorithms for adaptivelyadjusting control filters. Multi-channel ANC systems are typically required toattenuate unwanted noises in a large space. However, open-sourceimplementations of the multi-channel FxLMS (McFxLMS) and multi-channel FxNLMS(McFxNLMS) algorithm continue to be scarce. Therefore, this paper proposes asimple and effective implementation approach of the McFxLMS and McFxNLMSalgorithm. Motivated by the back-propagation process during neural networktraining, the McFxLMS and McFxNLMS algorithm can be implemented via automaticderivation mechanism. We implemented the two algorithms using the automaticderivation mechanism in PyTorch and made the source code available on GitHub.This implementation method can improve the practicality of multi-channel ANCsystems, which is expected to be widely used in ANC applications.

Implementation of Multi-channel Active Noise Control based on Back-propagation Mechanism