Multichannel active noise control (ANC) systems have been widely investigated for low-frequency noise attenuation over a spatial region. Using a conventional centralized control strategy based on the multichannel filtered- x least mean square (F x LMS) algorithm has been demonstrated to be effective for multichannel ANC systems, but the high computational burden restricts its practical applications. Meanwhile, a decentralized control strategy suffers from stability problems although it has been successful in reducing the computational load. Recently, distributed control strategies, such as the multitask diffusion adaptation scheme, have been introduced to ANC systems and shown to mitigate the stability problems in decentralized systems. However, distributed ANC systems using the diffusion F x LMS algorithm require strong symmetry of acoustic paths because of the dependence on node-based adaptation and neighborhood-based combination. To overcome this limitation, this paper proposes an Augmented Diffusion F x LMS algorithm with neighborhood-based adaptation and node-based combination. A theoretical formulation and convergence analysis are presented and simulations are performed to compare the proposed algorithm with existing ones under different system configurations for tonal, multi-tonal, narrowband and broadband signals. Simulation results demonstrate that the proposed algorithm has the same noise reduction performance as centralized method even if the acoustic paths are strongly asymmetrical, which is superior over existing distributed multitask diffusion strategy.

Accelerating beams have attracted considerable research interest due to their peculiar properties and various applications. Although there have been numerous research on the generation and application of accelerating light beams, few results have been published on the generation of accelerating acoustic beams. Here we report on the experimental observation of accelerating acoustic beams along arbitrary convex trajectories. The desired trajectory is projected to the spatial phase profile on the boundary which is discretized and sampled spatially. The sound field distribution is formulated with the Green function and the integral equation method. Both the paraxial and the non-paraxial regimes are examined and observed in the experiments. The effect of obstacle scattering in the sound field is also investigated and the results demonstrate that the approach is robust against obstacle scattering. The realization of accelerating acoustic beams will have an impact on various applications where acoustic information and energy are required to be delivered along an arbitrary convex trajectory.

This letter introduces a database of Room Impulse Responses (RIRs) measured in seven different rooms for multizone sound field reproduction research in various acoustic environments. A circular array of 60 loudspeakers was installed in each room, with two microphone arrays placed sequentially in five different zones inside the loudspeaker array. A total of 260 400 RIRs were measured to establish the database. As a demonstration application of the database for multizone sound field reproduction, simulations were performed on the pressure matching and acoustic contrast control methods to investigate how a system optimized with the RIRs measured in one room would perform in other rooms.

Sound field reconstruction (SFR) augments the information of a sound field captured by a microphone array. Using basis function decomposition, conventional SFR methods are straightforward and computationally efficient but may require more microphones than needed to measure the sound field. Recent studies show that pure data-driven and learning-based methods are promising in some SFR tasks, but they are usually computationally heavy and may fail to reconstruct a physically valid sound field. This paper proposes a compact acoustics-informed neural network (AINN) method for SFR, whereby the Helmholtz equation is exploited to regularize the neural network. As opposed to pure data-driven approaches that solely rely on measured sound pressures, the integration of the Helmholtz equation improves robustness of the neural network against variations during the measurement processes and prompts the generation of physically valid reconstructions. The AINN is designed to be compact and able to predict not only the sound pressures but also sound pressure gradients within a spatial region of interest based on measured sound pressures along the boundary. Experiments with acoustic transfer functions measured in different environments demonstrate the superiority of the AINN method over the traditional cylindrical harmonics and singular value decomposition methods.

Belt conveyor systems are essential in industries like automotive, aerospace, power generation, and heavy machinery, with idlers playing a crucial role in ensuring the smooth movement of materials. However, constant operation in noisy environments accelerates wear and tear on idlers and obscures early signs of malfunction, such as grinding or rattling from loose parts. This challenge makes early fault detection difficult, increasing downtime and maintenance costs. Therefore, timely and accurate fault detection is vital to prevent severe system issues, ensure optimal performance, and avoid unexpected breakdowns and costly production interruptions. Intelligent Fault Detection (IFD) using artificial intelligence (AI) methods has emerged as a solution, with machine learning techniques like convolutional neural networks (CNNs) proving effective. This study uses YAMNet, initially designed for Sound Event Detection (SED), to identify faults in belt conveyor idlers by analyzing their unique acoustic signatures. We enhance detection capabilities by extracting temporal features from YAMNet-generated embeddings using Bidirectional Long-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU), augmented with a soft attention mechanism. These features are evaluated using XGBoost, achieving an impressive 90% accuracy in fault detection across idler test sets. Our approach was rigorously compared to the VGGish model and validated on the publicly available MIMII dataset, where it demonstrated superior performance with AUC scores of 0.8355 for fans, 0.9414 for pumps, 0.9265 for valves, and 0.9703 for sliders. These results significantly improve over baseline scores, with increases of 19.36% for fans, 38.44% for pumps, 74.81% for valves, and 38.61% for sliders. This advancement represents a significant step forward in conveyor system diagnostics, providing a robust solution for enhancing industrial safety and operational efficiency.

Recent studies on diffusion adaptation for distributed active noise control (DANC) systems have attracted significant research interest due to their balance between computational burden and stability compared to conventional centralized and decentralized adaptation schemes. The conventional multitask diffusion FxLMS algorithm assumes that the converged solutions of all control filters are consistent to each other, which is unrealistic in practice hence results in inferior performance in noise reduction. An augmented diffusion FxLMS algorithm has been proposed to overcome this problem, which adopts a neighborhood-wide adaptation and node-based combination approach to mitigate the bias in the converged solution of the multitask diffusion algorithms. However, the improvement comes at the expense of a higher computational burden and communication cost. All existing DANC systems, including the multitask and augmented diffusion algorithms, assume one-way communication between nodes. By contrast, this paper proposes a bidirectional communication scheme for the augmented diffusion algorithm to further reduce the memory requirement, computational burden, and communication cost. Simulation results in the free field and with measured room impulse responses both demonstrate that the proposed augmented diffusion algorithm with bidirectional communication can achieve a faster convergence speed than that based on one-way communication with a lower memory, computation, and communication burden.