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

Deep Learning-Based Active Trim Panels for Enhanced Aircraft Interior Noise Control

Active noise control (ANC) trim panels offer an effective solution to suppress multi-tonal noise in aircraft. The selective fixed-filter ANC (SFANC) method, characterized by low computational complexity, high robustness and rapid response, is suitable to handle multi-tonal engine noise that varies in frequency due to changes in the rotational speed of the engine shaft. However, real-world conditions introduce variations in lining temperature, altering acoustic and structural paths and degrading noise reduction performance. To address this challenge, a temperature-perceptive SFANC (TP-SFANC) approach is proposed that employs a lightweight one-dimensional convolutional neural network (1D CNN) trained using a multi-task learning strategy. By processing both reference and error signals, the 1D CNN learns frequency and temperature characteristics to dynamically select the optimal control filter. Numerical simulations demonstrate the effectiveness of the proposed method in attenuating multi-tonal noise across varying frequencies and lining temperatures.

Active Noise Control using Deep Learning Methods

Digital Predistortion for Non-Differentiable Nonlinear Systems: A Reinforcement Learning Framework With Transfer Learning

This paper proposes a reinforcement learning (RL)-based digital pre-distortion (DPD) method to mitigate pattern-dependent nonlinearities in optical transmitters. The RL agent is configured with an optimized symbol window size, allowing it to effectively model pattern-dependent nonlinearities introduced by the digital-to-analog converter (DAC) and power amplifier (PA). To assess its performance, the peak-to-peak output voltage ( $V_{pp}$ ) of the DAC is swept, and the results are compared with conventional DPD methods, including non–machine learning (ML) techniques such as linear DPD, Volterra series, and look-up tables, as well as ML-based approaches such as indirect learning architecture (ILA) and direct learning architecture (DLA). Our analysis demonstrates that the RL-based approach, enabled by an effective neural network initialization strategy, surpasses traditional methods and ML-based DPD schemes such as DLA and ILA. Furthermore, we incorporate transfer learning (TL) into the RL framework to reduce training complexity across different $V_{pp}$ levels without full retraining, achieving substantial complexity reduction. The results demonstrate that combining RL with TL provides a scalable and efficient solution for compensating pattern-dependent nonlinearities in high-speed optical communications.

A CONCEPTUAL STUDY ON ACTIVE NOISE CANCELLATION: FROM ADAPTIVE ALGORITHMS TO DEEP LEARNING APPROACHES

Active Noise Cancellation (ANC) began as a theoretical idea in the 1930s and has now spread to include broad uses in consumer electronics, industrial, and automotive industries. This conceptual study provides a thorough explanation of ANC technology spanning more than 90 years of development, from Paul Lueg's early 1936 patent to the most recent deep learning methods. Adaptive filtering algorithms such as LMS, NLMS, FxLMS, FuLMS, and RLS variants are closely examined, along with new neural network architectures like Convolutional Recurrent Networks (CRN) and Attentive Recurrent Networks (ARN), which achieve sub-4 ms latency with noise cancellation improvements of 5-6 dB over traditional methods. The noise reduction parameters, computational complexity, power consumption, and convergence characteristics are all included in the thorough performance comparisons presented in this study. Using benchmark datasets like NOISEX-92 and DEMAND for algorithm evaluation, we examine feedforward, feedback, and hybrid system designs used in consumer headphones, automobile cabins, aviation, and HVAC systems. Given their greater resistance to broadband noise and nonlinear distortions, our conceptual study shows that deep learning approaches are the ANC technology of the future. However, conventional FxLMS maintains the industry standard because of its computational efficiency (O(N) complexity).

A gradient-enhanced physics-informed neural network with adaptive loss weighting for high-dimensional non-linear sine-Gordon problems.

This paper proposes a modified physics-informed neural network (PINN), known as Adaptive Weighted Loss Gradient-Enhanced PINNs (AWL-gPINNs) to the numerical approximation of high-dimensional nonlinear sine-Gordon equations (SGEs). The proposed algorithm is an extension of the typical PINN formulation, i.e. it adds gradient-based residual constraints and an adaptive weighting strategy in order to balance the importance of PDE residual terms, initial conditions, and boundary conditions in training. The existing governing SGE is reduced to a coupled first-order form, which allows the automatic differentiation to be easily integrated to evaluate higher-order derivatives. The resulting composite loss functional consists of the residual and gradient-regularization terms that have trainable weights, which reduce the issue of stiffness and imbalance in multi-objective optimization. Benchmark problems of 3D to 20D damped and undamped SGEs in both long and short time domains are extensively numerically experimented with. The findings show that AWL-gPINNs perform much better than standard PINNs and a variety of existing algorithms and obtain orders of error reduction between 1e-3 -1e-2 and 1e-5-1e-4. The technique also demonstrates rapid convergence, increased training robustness, and stability across different collocation densities, noise perturbations, and initialization conditions. Moreover, when the proposed method is compared to state-of-the-art variants of PINN, it is established that the method is superior to the current methods in a variety of high-dimensional PDEs with very small error magnitudes, even in the 20D case. The efficacy, robustness, and practical efficiency of the suggested AWL-gPINN framework for high-dimensional nonlinear SGE are further validated by ablation studies and multi-seed stability assessments. The results support that the proposed AWL-gPINNs is a scalable and successful technique for high-dimensional nonlinear PDE solutions.

Adaptive Gating Dynamics for Layer-Wise Sparsity Learning and Neural Network Pruning

Deep neural networks often contain substantial parameter redundancy, resulting in unnecessary computational cost and energy consumption. This work presents a dynamically adaptive gating mechanism for learning layer-wise sparsity through differentiable masking. In the proposed framework, each network parameter is associated with a self-learning gate that controls its contribution during training. The gating function follows a progressive soft-to-hard transition in which the slope is gradually annealed, enabling the model to move smoothly from continuous parameter weighting to near-binary pruning decisions while simultaneously learning an adaptive threshold parameter. The framework is evaluated across multilayer perceptrons (MLPs), deep neural networks (DNNs), Tabular Transformer models, and benchmarked against $\mathrm{L}_{0}$ regularization and Variational Dropout on convolutional neural networks. Experimental results demonstrate substantial model compression without degrading predictive performance, and in some cases improving it. On MNIST, the gated MLP retains only 14% of weights (86% pruning) while maintaining 98% accuracy. The gated DNN and Tab Transformer similarly outperform their dense counterparts while retaining only 46% and 46.22% of parameters, respectively. Benchmarking further shows that the proposed adaptive gating achieves competitive or superior accuracy-sparsity trade-offs compared with $\mathbf{L}_{0}$ regularization and Variational Dropout. These results demonstrate that the proposed dynamically adaptive gating framework provides an efficient and interpretable pathway for sparsity learning, enabling high-performing lightweight neural network deployment.