U-Shaped WaveNet With Adaptive Volterra Neural Networks for Active Noise Control

Abstract

Deep learning-based active noise control (ANC) algorithms demonstrate superior potential over traditional methods in addressing nonlinear distortion. Although recent deep learning approaches incorporating Volterra Neural Networks (VNNs) have been optimized to tackle nonlinearities, there remains room for further improvement: (1) utilizing element-wise addition in skip connections across different feature processing stages increases the risk of feature aliasing or suppression; (2) directly introducing higher-order terms of the Volterra series is prone to causing overfitting; and (3) models trained under a singular nonlinear condition struggle to adapt to real-world scenarios with varying nonlinearities. To address these issues, this letter proposes a novel time-domain ANC framework. While retaining the modeling capabilities of WaveNet and VNNs, the proposed framework integrates the U-shaped structure and incorporates an adaptive gating mechanism for the higher-order Volterra terms. The proposed method is compared with a state-of-the-art deep learning framework. Additionally, the model is evaluated under several controlled nonlinear conditions, and comprehensive ablation studies are conducted. Simulation results demonstrate that the proposed algorithm outperforms existing end-to-end representative Deep Neural Network (DNN) methods, and ablation studies confirm the effectiveness of the proposed modules.