arXiv subjects

Dongyuan Shi

Publications and source records attributed to Dongyuan Shi.

49 records · Page 3Linked to original sources

arXiv2023

Practical Active Noise Control: Restriction of Maximum Output Power

This paper presents some recent algorithms developed by the authors for real-time adaptive active noise (AANC) control systems. These algorithms address some of the common challenges faced by AANC systems, such as speaker saturation, system divergence, and disturbance rejection. Speaker saturation can introduce nonlinearity into the adaptive system and degrade the noise reduction performance. System divergence can occur when the secondary speaker units are over-amplified or when there is a disturbance other than the noise to be controlled. Disturbance rejection is important to prevent the adaptive system from adapting to unwanted signals. The paper provides guidelines for implementing and operating real-time AANC systems based on these algorithms.

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arXiv2023

Anti-noise window: Subjective perception of active noise reduction and effect of informational masking

Reviving natural ventilation (NV) for urban sustainability presents challenges for indoor acoustic comfort. Active control and interference-based noise mitigation strategies, such as the use of loudspeakers, offer potential solutions to achieve acoustic comfort while maintaining NV. However, these approaches are not commonly integrated or evaluated from a perceptual standpoint. This study examines the perceptual and objective aspects of an active-noise-control (ANC)-based "anti-noise" window (ANW) and its integration with informational masking (IM) in a model bedroom. Forty participants assessed the ANW in a three-way interaction involving noise types (traffic, train, and aircraft), maskers (bird, water), and ANC (on, off). The evaluation focused on perceived annoyance (PAY; ISO/TS 15666), perceived affective quality (ISO/TS 12913-2), loudness (PLN), and included an open-ended qualitative assessment. Despite minimal objective reduction in decibel-based indicators and a slight increase in psychoacoustic sharpness, the ANW alone demonstrated significant reductions in PAY and PLN, as well as an improvement in ISO pleasantness across all noise types. The addition of maskers generally enhanced overall acoustic comfort, although water masking led to increased PLN. Furthermore, the combination of ANC with maskers showed interaction effects, with both maskers significantly reducing PAY compared to ANC alone.

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arXiv2023

Active Noise Control in The New Century: The Role and Prospect of Signal Processing

Since Paul Leug's 1933 patent application for a system for the active control of sound, the field of active noise control (ANC) has not flourished until the advent of digital signal processors forty years ago. Early theoretical advancements in digital signal processing and processors laid the groundwork for the phenomenal growth of the field, particularly over the past quarter-century. The widespread commercial success of ANC in aircraft cabins, automobile cabins, and headsets demonstrates the immeasurable public health and economic benefits of ANC. This article continues where Elliott and Nelson's 1993 Signal Processing Magazine article and Elliott's 1997 50th anniversary commentary on ANC left off, tracing the technical developments and applications in ANC spurred by the seminal texts of Nelson and Elliott (1991), Kuo and Morgan (1996), Hansen and Snyder (1996), and Elliott (2001) since the turn of the century. This article focuses on technical developments pertaining to real-world implementations, such as improving algorithmic convergence, reducing system latency, and extending control to non-stationary and/or broadband noise, as well as the commercial transition challenges from analog to digital ANC systems. Finally, open issues and the future of ANC in the era of artificial intelligence are discussed.

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arXiv2023

A Computation-efficient Online Secondary Path Modeling Technique for Modified FXLMS Algorithm

This paper proposes an online secondary path modelling (SPM) technique to improve the performance of the modified filtered reference Least Mean Square (FXLMS) algorithm. It can effectively respond to a time-varying secondary path, which refers to the path from a secondary source to an error sensor. Unlike traditional methods, the proposed approach switches modes between adaptive ANC and online SPM, eliminating the use of destabilizing components such as auxiliary white noise or additional filters, which can negatively impact the complexity, stability, and noise reduction performance of the ANC system. The system operates in adaptive ANC mode until divergence is detected due to secondary path changes. At this moment, it switches to SPM mode until the path is remodeled and then returns to ANC mode. Furthermore, numerical simulations in the paper demonstrate that the proposed online technique effectively copes with the secondary path variations.

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arXiv2023

MOV-Modified-FxLMS algorithm with Variable Penalty Factor in a Practical Power Output Constrained Active Control System

Practical Active Noise Control (ANC) systems typically require a restriction in their maximum output power, to prevent overdriving the loudspeaker and causing system instability. Recently, the minimum output variance filtered-reference least mean square (MOV-FxLMS) algorithm was shown to have optimal control under output constraint with an analytically formulated penalty factor, but it needs offline knowledge of disturbance power and secondary path gain. The constant penalty factor in MOV-FxLMS is also susceptible to variations in disturbance power that could cause output power constraint violations. This paper presents a new variable penalty factor that utilizes the estimated disturbance in the established Modified-FxLMS (MFxLMS) algorithm, resulting in a computationally efficient MOV-MFxLMS algorithm that can adapt to changes in disturbance levels in real-time. Numerical simulation with real noise and plant response showed that the variable penalty factor always manages to meet its maximum power output constraint despite sudden changes in disturbance power, whereas the fixed penalty factor has suffered from a constraint mismatch.

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arXiv2023

Real-time modelling of observation filter in the Remote Microphone Technique for an Active Noise Control application

The remote microphone technique (RMT) is often used in active noise control (ANC) applications to overcome design constraints in microphone placements by estimating the acoustic pressure at inconvenient locations using a pre-calibrated observation filter (OF), albeit limited to stationary primary acoustic fields. While the OF estimation in varying primary fields can be significantly improved through the recently proposed source decomposition technique, it requires knowledge of the relative source strengths between incoherent primary noise sources. This paper proposes a method for combining the RMT with a new source-localization technique to estimate the source ratio parameter. Unlike traditional source-localization techniques, the proposed method is capable of being implemented in a real-time RMT application. Simulations with measured responses from an open-aperture ANC application showed a good estimation of the source ratio parameter, which allows the observation filter to be modelled in real-time.

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arXiv2023

A Momentum Two-gradient Direction Algorithm with Variable Step Size Applied to Solve Practical Output Constraint Issue for Active Noise Control

Active noise control (ANC) has been widely utilized to reduce unwanted environmental noise. The primary objective of ANC is to generate an anti-noise with the same amplitude but the opposite phase of the primary noise using the secondary source. However, the effectiveness of the ANC application is impacted by the speaker's output saturation. This paper proposes a two-gradient direction ANC algorithm with a momentum factor to solve the saturation with faster convergence. In order to make it implemented in real-time, a computation-effective variable step size approach is applied to further reduce the steady-state error brought on by the changing gradient directions. The time constant and step size bound for the momentum two-gradient direction algorithm is analyzed. Simulation results show that the proposed algorithm performs effectively in the time-unvaried and time-varied environment.

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arXiv2023

A practical distributed active noise control algorithm overcoming communication restrictions

By assigning the massive computing tasks of the traditional multichannel active noise control (MCANC) system to several distributed control nodes, distributed multichannel active noise control (DMCANC) techniques have become effective global noise reduction solutions with low computational costs. However, existing DMCANC algorithms simply complete the distribution of traditional centralized algorithms by combining neighbour nodes' information but rarely consider the degraded control performance and system stability of distributed units caused by delays and interruptions in communication. Hence, this paper develops a novel DMCANC algorithm that utilizes the compensation filters and neighbour nodes' information to counterbalance the cross-talk effect between channels while maintaining independent weight updating. Since the neighbours' information required barely affects the local control filter updating in each node, this approach can tolerate communication delay and interruption to some extent. Numerical simulations demonstrate that the proposed algorithm can achieve satisfactory noise reduction performance and high robustness to real-world communication challenges.

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arXiv2023

Automatic Generation of Topology Diagrams for Strongly-Meshed Power Transmission Systems

Topology diagrams are widely seen in power system applications, but their automatic generation is often easier said than done. When facing power transmission systems with strongly-meshed structures, existing approaches can hardly produce topology diagrams catering to the aesthetics of readers. This paper proposes an integrated framework for generating aesthetically-pleasing topology diagrams for power transmission systems. Input with a rough layout, the framework first conducts visibility region analysis to reduce line crossings and then solves a mixed-integer linear programming problem to optimize the arrangement of nodes. Given that the complexity of both modules is pretty high, simplification heuristics are also proposed to enhance the efficiency of the framework. Case studies on several power transmission systems containing up to 2,046 nodes demonstrate the capability of the proposed framework in generating topology diagrams conforming to aesthetic criteria in the power system community. Compared with the widespread force-directed algorithm, the proposed framework can preserve the relative positions of nodes in the original layout to a great extent, which significantly contributes to the identification of electrical elements on the diagrams. Meanwhile, the time consumption is acceptable for practical applications.

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arXiv2023

Deep Generative Fixed-filter Active Noise Control

Due to the slow convergence and poor tracking ability, conventional LMS-based adaptive algorithms are less capable of handling dynamic noises. Selective fixed-filter active noise control (SFANC) can significantly reduce response time by selecting appropriate pre-trained control filters for different noises. Nonetheless, the limited number of pre-trained control filters may affect noise reduction performance, especially when the incoming noise differs much from the initial noises during pre-training. Therefore, a generative fixed-filter active noise control (GFANC) method is proposed in this paper to overcome the limitation. Based on deep learning and a perfect-reconstruction filter bank, the GFANC method only requires a few prior data (one pre-trained broadband control filter) to automatically generate suitable control filters for various noises. The efficacy of the GFANC method is demonstrated by numerical simulations on real-recorded noises.

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arXiv2022

Implementation of Multi-channel Active Noise Control based on Back-propagation Mechanism

Active noise control (ANC) systems can efficiently attenuate low-frequency noises by introducing anti-noises to combine with the unwanted noises. In ANC systems, the filtered-x least mean square (FxLMS) and filtered-X normalized least-mean-square (FxNLMS) algorithm are well-known algorithms for adaptively adjusting control filters. Multi-channel ANC systems are typically required to attenuate unwanted noises in a large space. However, open-source implementations of the multi-channel FxLMS (McFxLMS) and multi-channel FxNLMS (McFxNLMS) algorithm continue to be scarce. Therefore, this paper proposes a simple and effective implementation approach of the McFxLMS and McFxNLMS algorithm. Motivated by the back-propagation process during neural network training, the McFxLMS and McFxNLMS algorithm can be implemented via automatic derivation mechanism. We implemented the two algorithms using the automatic derivation mechanism in PyTorch and made the source code available on GitHub. This implementation method can improve the practicality of multi-channel ANC systems, which is expected to be widely used in ANC applications.

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arXiv2022

Performance Evaluation of Selective Fixed-filter Active Noise Control based on Different Convolutional Neural Networks

Due to its rapid response time and a high degree of robustness, the selective fixed-filter active noise control (SFANC) method appears to be a viable candidate for widespread use in a variety of practical active noise control (ANC) systems. In comparison to conventional fixed-filter ANC methods, SFANC can select the pre-trained control filters for different types of noise. Deep learning technologies, thus, can be used in SFANC methods to enable a more flexible selection of the most appropriate control filters for attenuating various noises. Furthermore, with the assistance of a deep neural network, the selecting strategy can be learned automatically from noise data rather than through trial and error, which significantly simplifies and improves the practicability of ANC design. Therefore, this paper investigates the performance of SFANC based on different one-dimensional and two-dimensional convolutional neural networks. Additionally, we conducted comparative analyses of several network training strategies and discovered that fine-tuning could improve selection performance.

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arXiv2022

A Hybrid SFANC-FxNLMS Algorithm for Active Noise Control based on Deep Learning

The selective fixed-filter active noise control (SFANC) method selecting the best pre-trained control filters for various types of noise can achieve a fast response time. However, it may lead to large steady-state errors due to inaccurate filter selection and the lack of adaptability. In comparison, the filtered-X normalized least-mean-square (FxNLMS) algorithm can obtain lower steady-state errors through adaptive optimization. Nonetheless, its slow convergence has a detrimental effect on dynamic noise attenuation. Therefore, this paper proposes a hybrid SFANC-FxNLMS approach to overcome the adaptive algorithm's slow convergence and provide a better noise reduction level than the SFANC method. A lightweight one-dimensional convolutional neural network (1D CNN) is designed to automatically select the most suitable pre-trained control filter for each frame of the primary noise. Meanwhile, the FxNLMS algorithm continues to update the coefficients of the chosen pre-trained control filter at the sampling rate. Owing to the effective combination of the two algorithms, experimental results show that the hybrid SFANC-FxNLMS algorithm can achieve a rapid response time, a low noise reduction error, and a high degree of robustness.

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