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

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

A feedback-guided DNN-based controller fusion framework for robust fixed-parameter ANC that combines a causal WaveNet controller with a feedback-guided mixture-of-experts (MoE) module, where a gating network estimates the weights of multiple pre-trained FIR experts according to the current acoustic condition.

Adaptive Momentum Enhanced Distributed Multichannel Active Noise Control for Faster Convergence under Communication Delays

Distributed multichannel active noise control (DMCANC) reduces the computational burden of centralized ANC systems by distributing processing tasks across multiple nodes, while requiring information exchange to achieve satisfactory global noise reduction. To improve robustness under communication delays, the auto-shrink step size mixed-gradients filtered reference LMS (ASSS-MGDFxLMS) algorithm has been proposed. However, the reduced step size inevitably slows convergence. In this work, an adaptive momentum term is introduced to accelerate convergence, where cosine similarity is used to evaluate the alignment between the instantaneous gradient and the momentum component and dynamically adjust the momentum parameter. This design accelerates convergence when the directions are consistent while preserving stability under delayed communication. Simulation results demonstrate that the proposed adaptive momentum ASSS-MGDFxLMS (AMAS-MGDFxLMS) algorithm achieves faster convergence than ASSS-MGDFxLMS while maintaining stable and effective noise reduction performance.

Computationally Efficient Deep Learning Approach Using IQ-MobNet for Radar DoA Estimation in Limited Snapshot Conditions

This paper presents a computationally efficient deep learning framework for accurate direction-of-arrival (DoA) estimation in portable radar applications. Leveraging a MobileNet architecture, the proposed model directly processes raw in-phase and quadrature-phase (IQ) data, enabling more effective learning of both spatial and temporal features. This direct input approach enhances DoA estimation accuracy, particularly under challenging conditions such as low signal-to-noise ratio (SNR) and limited snapshot scenarios. A unified training strategy is adopted for both single-source and multi-source target detection, ensuring consistency and robustness. Comprehensive simulation experiments demonstrate the proposed model’s competitive and robust performance across various conditions, including different SNR levels, closely spaced targets, and random off-grid angles. It also shows that our method achieves performance comparable to or better than recent deep learning approaches in several challenging scenarios, establishing its potential for resource-constrained environments where only low snapshot data are available. The proposed IQ-MobNet DoA estimation model achieves this competitive performance with substantially lower computational complexity, requiring only 0.24 million parameters and 0.42 million Floating Point Operations (FLOPs), representing a reduction of over 96% compared to the recent neural network models. To ensure practical applicability, the proposed IQ-MobNet framework is validated using real-world measured data, confirming its robustness beyond simulated environments.

Distributed Multichannel Wiener Filtering for Topology-Unconstrained Wireless Acoustic Sensor Networks

This paper introduces the topology-independent distributed multichannel Wiener filter (TI-dMWF), a novel algorithm for distributed node-specific signal estimation in wireless acoustic sensor networks (WASNs) with unconstrained topologies. The TI-dMWF enables each node in the network to compute its centralized multichannel Wiener filter solution by exchanging only low-dimensional fused signals, without requiring iterative estimation, unlike state-of-the-art approaches such as the topology-independent distributed adaptive node-specific signal estimation (TI-DANSE) algorithm. The TI-dMWF is proven optimal when each source is observed by either all nodes or only one node. Theoretical analysis and numerical simulations confirm that it achieves centralized estimation performance in a single run. Its latency as a function of the pruned-tree depth and its computational complexity are also analyzed. Its robustness is assessed in reverberant-room simulations under estimated second-order statistics, various network topologies, and deviations from the assumed observability model.

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.

Tree-Structured Deep Neural Networks for High-Resolution DOA Estimation in Massive MIMO Systems