WaveNet-Volterra Neural Networks for Active Noise Control: A Fully Causal Approach

Active Noise Control (ANC) systems are challenged by nonlinear distortions, which degrade the performance of traditional adaptive filters. While deep learning-based ANC algorithms have emerged to address nonlinearity, existing approaches often overlook critical limitations: (1) end-to-end Deep Neural Network (DNN) models frequently violate causality constraints inherent to real-time ANC applications; (2) many studies compare DNN-based methods against simplified or low-order adaptive filters rather than fully optimized high-order counterparts. In this letter, we propose a causality-preserving time-domain ANC framework that synergizes WaveNet with Volterra Neural Networks (VNNs), explicitly addressing system nonlinearity while ensuring strict causal operation. Unlike prior DNN-based approaches, our method is benchmarked against both state-of-the-art deep learning architectures and rigorously optimized high-order adaptive filters, including Wiener solutions. Simulations demonstrate that the proposed framework achieves superior performance over existing DNN methods and traditional algorithms, revealing that prior claims of DNN superiority stem from incomplete comparisons with suboptimal traditional baselines. Source code is available at https://github.com/Lu-Baihh/WaveNet-VNNs-for-ANC.git.

Deep Learning-Based Approach for Identification and Compensation of Nonlinear Distortions in Parametric Array Loudspeakers

Compared to traditional electrodynamic loudspeakers, the parametric array loudspeaker (PAL) offers exceptional directivity for audio applications but suffers from significant nonlinear distortions due to its inherent intricate demodulation process. The Volterra filter-based approaches have been widely used to reduce these distortions, but the effectiveness is limited by its inverse filter's capability. Specifically, its pth-order inverse filter can only compensate for nonlinearities up to the pth order, while the higher-order nonlinearities it introduces continue to generate lower-order harmonics. In contrast, this paper introduces the modern deep learning methods for the first time to address nonlinear identification and compensation for PAL systems. Specifically, a feedforward variant of the WaveNet neural network, recognized for its success in audio nonlinear system modeling, is utilized to identify and compensate for distortions in a double sideband amplitude modulation-based PAL system. Experimental measurements from 250 Hz to 8 kHz demonstrate that our proposed approach significantly reduces both total harmonic distortion and intermodulation distortion of audio sound generated by PALs, achieving average reductions to 4.55% and 2.47%, respectively. This performance is notably superior to results obtained using the current state-of-the-art Volterra filter-based methods. Our work opens new possibilities for improving the sound reproduction performance of PALs.