Low-Complexity Machine Learning Models for Active Noise Control in Nonlinear Systems

Active Noise Control (ANC) systems are typically based on adaptive filters. However, electroacoustic transducers and their associated electronic components often exhibit nonlinear behaviors that linear controllers cannot accurately model, resulting in suboptimal performance. While neural networks and other advanced models have been proposed to address these limitations, their high computational demands and inherent latency frequently restrict real-time deployment. This work investigates the use of lightweight, computationally efficient machine learning (ML) models that operate on a sample-by-sample basis using simple digital operators. The proposed models are applied to ANC under nonlinear conditions, including distortions in the primary path, the secondary path, and the reference signal. The approach enhances noise attenuation while preserving low computational complexity, thereby enabling real-time implementation on embedded systems. Simulation results confirm the effectiveness of the method across a variety of nonlinear scenarios, demonstrating superior noise reduction and control accuracy compared to conventional linear ANC schemes, and achieving this at a significantly lower cost than alternative nonlinear approaches.