Feedback-Guided DNN-Based Controller Fusion for Robust Fixed-Parameter Active Noise Control

Active Noise Control using Deep Learning Methods

Model-Agnostic Meta-Learning Initialization for Distributed Multichannel Active Noise Control

Distributed multichannel active noise control (DMCANC) has emerged as a scalable framework for large-area noise reduction, where multiple nodes operate local single-channel ANC controllers and exchange essential information to achieve global control. A key limitation of existing DMCANC implementations lies in their reliance on zero or random initialization, which leads to slow convergence of adaptive filters and restricts the efficiency of internode collaboration. To address this issue, this paper introduces a model-agnostic meta-learning (MAML) based initialization strategy for DMCANC. By aggregating heterogeneous acoustic characteristics across nodes-ncluding primary and secondary paths-a MAML framework is trained to learn an initialization that generalizes effectively across distributed ANC systems. The MAML initialization is then deployed to all nodes to improve convergence speed under both stationary and time-varying noise conditions. Numerical simulations applied on broadband and real-world noise demonstrate that the proposed algorithms achieves substantially faster convergence and improved noise reduction performance compared with conventional DMCANC, highlighting the potential of MAML initialization as an effective method for large-scale ANC.

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.

Synergistic Quantization for Generalized Cauchy Adaptive Filters in Acoustic Echo Cancellation

This letter proposes GC-SQMCC, a computationally efficient robust adaptive filtering algorithm tailored for fixed-point acoustic echo cancellation (AEC) under impulsive noise. Departing from signal-driven quantizers, the proposed scheme synergistically aligns a nonlinear compression-mapping quantizer with the influence function of the generalized Cauchy (GC) kernel, allocating fine resolution near zero and gracefully coarsening for large impulses. An adaptive error envelope tracker (AEET) normalizes the dynamic range online, while a synergistic look-up table (LUT) fuses the kernel weighting and error scaling into a single memory access, enabling 16-bit integer arithmetic during the per-sample filtering stage by moving divisions and transcendental operations to the infrequent LUT-update process. A threshold-based adaptive LUT update (ALUT-U) further decouples high-frequency filtering from low-frequency grid adjustment. Mean and mean-square stability conditions are derived, and an asymptotic steady-state MSD is derived under high-resolution assumptions. Simulations on AEC under single-talk and double-talk scenarios show that GC-SQMCC achieves lower steady-state MSD than competing quantized baselines under various configurations.

Robust Real-Time DOA Estimation for Outdoor Vehicle Acoustic Sources Using Dynamic-Pruning GCC-PHAT and Adaptive Forgetting Factor OPAST-MUSIC

In outdoor road environments, vehicle acoustic source direction-of-arrival (DOA) estimation is challenged by a low signal-to-noise ratio (SNR), dynamic-noise interference, and stringent real-time requirements. Under such conditions, conventional methods often struggle to achieve an effective balance among estimation accuracy, computational efficiency, and robustness against noise. To address this issue, this paper proposes a DOA estimation method that integrates a dynamic-pruning strategy with an adaptive subspace tracking mechanism. The proposed approach reduces computational complexity while enhancing algorithmic stability in complex and time-varying noise environments. Extensive experiments conducted on simulated data, the LOCATA dataset, and real-world outdoor road measurements demonstrate that the proposed method achieves comparable or superior DOA accuracy relative to conventional approaches, while significantly reducing computational cost. Furthermore, it exhibits stronger stability and robustness in real-world static and dynamic vehicle localization scenarios. Our method achieves a more favorable trade-off among multiple performance metrics. The results show that this method has good engineering application potential in complex outdoor environments, and can provide a practical solution for real-world vehicle monitoring.

Data-driven adaptive gain tuning for the augmented homogeneous differentiator

Derivative estimation from noisy sensor measurements is a fundamental requirement in feedback control, and any such estimator is subject to an inherent trade-off between tracking speed and noise rejection. In the augmented homogeneous differentiator (AHD), this trade-off is governed by a single scalar gain. A large gain tracks fast transients but amplifies noise, whereas a small gain attenuates noise at the cost of a slower response, and no fixed value remains suitable when the signal statistics vary over time. This paper presents an adaptive-gain AHD in which a network combining convolutional, recurrent, and attention layers is trained offline and deployed online to predict, from a short window of the raw noisy signal, the smallest gain that meets a prescribed velocity-accuracy target. Using the raw noisy signal rather than the differentiator output as the network input avoids a direct feedback path from that output to the predictor. On a held-out window-level test set, the predictor attains R2=0.9834. In a frequency-sweep test, the adaptive scheme reduces the first-order velocity-estimation error by 99.3 % relative to a fixed gain tuned for the low-frequency regime. In a step test, it reduces the steady-state third-order derivative noise by 87.2 %. In closed-loop control of a variable-length pendulum, it reduces the control-input total variation by 32 % while preserving tracking accuracy.