Active Noise Control System Research Articles

Active Impulsive Noise Control with Missing Input Data Based on FxImdMCC Algorithm

In this study, we address the challenge of noise reduction in environments characterized by impulsive noise and missing input data in active noise control (ANC) systems, where existing algorithms often fail to deliver satisfactory results. Background noise, especially impulsive noise, poses a significant obstacle to signal processing and noise reduction. Furthermore, data loss during transmission or acquisition further complicates the noise reduction task. In this paper, a filtered-x imputation of the missing data maximum correntropy criterion (FxImdMCC) algorithm is proposed based on an imputation model, least mean square, and the maximum correntropy criterion (MCC), which can effectively reduce the impact of outliers and missing input data. The simulation results demonstrate the efficacy of the proposed FxImdMCC algorithm, which significantly outperforms existing algorithms in the context of active impulsive noise control.

Experimental study of a distributed active noise control system with multi-device nodes based on augmented diffusion strategy.

Recently, distributed active noise control (DANC) algorithms have been explored as a way to reduce computational complexity while ensuring system stability, thereby outperforming conventional centralized and decentralized algorithms. Most existing DANC algorithms assume that each node has only one pair of loudspeaker and microphone, limiting their flexibility in practical applications. In contrast, this paper proposes a DANC algorithm with general multi-device nodes based on the recently developed augmented diffusion strategy, allowing flexible and scalable ANC applications. A real-time distributed ANC system based on a multi-core digital signal processor platform is developed in order to compare the control performance of the proposed extended augmented diffusion algorithm with that of existing centralized, decentralized and augmented diffusion algorithms. Real-time experiments demonstrate that the proposed algorithm exhibits noise reduction performance consistent with that of the centralized algorithm while achieving lower global computational complexity and avoiding the system instability risk of the decentralized algorithm. Further, the new algorithm improves convergence speed and reduces the global communication cost compared to the previous augmented diffusion algorithm. Experimental results indicate the application potential of the proposed DANC algorithm for a generalized system configuration.

Euclidean direction search algorithm with maximum correntropy criterion for active noise control system

Euclidean direction search algorithm with maximum correntropy criterion for active noise control system

Selective collaboration in distributed FxLMS active noise control systems

This paper addresses the selection of the collaboration configuration on distributed active noise control (ANC) systems. ANC systems aim to cancel out acoustic noise within a listening area. In distributed systems, the control task is delegated among multiple acoustic control nodes that generate the control signals by filtering a noise reference signal. The coefficients of each node filter are iteratively calculated using the filtered-X LMS algorithm. The stability is achieved when the adaptive filters computed in each node converge to finite values. However, acoustic coupling among nodes could lead to instability (i.e., divergence). Collaboration among selected nodes may avoid this phenomenon, although not just any collaboration configuration guarantees network stability. On the other hand, a collaborative distributed system presents two drawbacks: the stability assessment is computationally expensive, and communication requirements increase with the number of collaborations among nodes. In this paper, we propose and discuss several methods to establish a collaboration configuration that ensures system stability. The optimal configuration, which is characterized by the minimal number of necessary collaborations between nodes, can be identified through exhaustive search. However, this approach incurs a high computational cost, particularly in networks with many nodes. To address this challenge, we introduce several heuristic methods aimed at efficiently obtaining stable configurations.

A non-linear local active noise control with integrated virtual sensing using an adaptive hybrid spline filter

Virtual sensing technique has been a promising means for local active noise control (ANC) to surpass the design constraint of error microphone placement at a desired location. Most existing systems which use virtual sensing employ an finite impulse response (FIR) filter based adaptive filter for achieving noise cancellation. However, the effectiveness of such ANC systems deteriorate in the presence of non-linearities in the system. In this paper, an attempt has been made to develop a class of non-linear ANC systems, with integrated virtual sensing, based on adaptive spline filters. These include a Wiener model, a Hammerstein model and a sandwich Hammerstein-Wiener based hybrid model of non-linear ANC. The learning algorithms for the proposed ANC schemes have been developed and it was observed that the adaptive nature of the activation functions allow the ANC system to shift from linear to non-linear and vice versa depending on the control scenario. The simulation results demonstrate that the hybrid spline based ANC system provides an improved noise reduction performance in non-linear scenarios in comparison with other ANC schemes.

A survey of integrating wireless technology into active noise control

Active Noise Control (ANC) is a widely adopted technology for reducing environmental noise across various scenarios. This paper focuses on enhancing noise reduction performance, particularly through the refinement of signal quality fed into ANC systems. We discuss the main wireless technique integrated into the ANC system equipped with some innovative algorithms in diverse environments. Instead of using microphone arrays, which increase the computation complexity of the ANC system, to isolate multiple noise sources to improve noise reduction performance, the application of the wireless technique avoids extra computation demand. Wireless transmissions of reference, error, and control signals are also applied to improve the convergence performance of the ANC system. Furthermore, this paper lists some wireless ANC applications, such as earbuds, headphones, windows, and headrests, underscoring their adaptability and efficiency in various settings.

Optimal selection of reference microphone for active noise control

This paper proposes two methods for selecting a reference microphone from multiple microphones, focusing on the correlation between the reference signal and the desired signal and the causality constraint of the signal, in order to effectively reduce noise arriving from various locations. In the first method, a threshold is set for the correlation between the reference signal and the desired signal, and a microphone that satisfies the causality constraint is selected as the reference microphone from microphones with correlation values that exceed this threshold. On the other hand, the second method selects a microphone with the highest correlation between the reference signal and the desired signal as the reference microphone among the microphones that satisfy the causality constraint. We demonstrate through simulation experiments that the active noise control system using these proposed methods can provide stable and effective noise reduction for noise arriving from various locations. It is also shown that both methods can reselect an appropriate reference microphone when the noise source moves.

Improved direct-parallel active noise control systems for applications with narrowband noises

Rotary machinery generates noises at the fundamental frequency and its harmonics. The direct-parallel form active noise control (ANC) system, which is capable of reducing multiple narrowband noises, uses many harmonically related sinusoids as the reference input. The idea of direct-parallel form ANC is to split these sinusoids into several mutually exclusive sets, so each set has frequencies spaced out as far as possible. This paper proposes an improved direct-parallel form method to separate the reference sinusoidal signals and to enhance the noise reduction performance. Several experiments regarding to the ANC with a muffler model is made to verify the performance of the proposed method.

GPU implementation of a fast active noise control algorithm with multiple reference signals

Traditional active noise control (ANC) systems are mostly implemented on digital signal processor (DSP). Due to the inadequate computational power of the DSP, especially in applications with multiple reference signals, the DSP is unable to update the optimal control filter for different types of noise. Hence, Graphics Processing Unit (GPU) has been incorporated to the ANC system to enhance its computational power. In this work, a DSP-CPU-GPU computing architecture is adopted to improve the computational performance of the ANC system and ensure the real-time signal processing. In addition, a fast ANC algorithm on the GPU is employed to update the control filters to handle different reference signals. Finally, the effectiveness of the method is verified by experiments in the condition of multiple reference signals.

Analysis of nonminimum phase components of noise control filters in active noise control

In the active noise control (ANC), it is known that the optimal filter becomes a noncausal filter due to the effect of the nonminimum phase components of the secondary path. We have investigated how to make the causal optimal filter by replacing the nonminimum phase components with the frequency components of the primary path. Through the computer simulations, we have confirmed that this filter maintained some noise reduction performance and can be used as a quasi-optimal filter. In this paper, as another approach, we attempt to identify noncausal components using the pre-trained adaptive noise control filter. The nonminimum phase components of the secondary path exist in the frequency band below the lowest resonance frequency of the secondary loudspeaker. Based on this fact, we design a noise control filter by using band-limited white noise only with the low frequency components below the lowest resonance frequency in advance. Then, we design another adaptive noise control filter by using the band-limited white noise with high frequency components above the lowest resonance frequency. Finally, we combine these filters in the frequency domain. Through the computer simulations, we show the difficulty to handle the nonminimum phase components in the ANC system.

Computation-efficient virtual sensing approach with multichannel adjoint least mean square algorithm

Multichannel active noise control (ANC) systems are designed to create a large zone of quietness (ZoQ) around the error microphones, however, the placement of these microphones often presents challenges due to physical limitations. Virtual sensing technique that effectively suppresses the noise far from the physical error microphones is one of the most promising solutions. Nevertheless, the conventional multichannel virtual sensing ANC (MVANC) system based on the multichannel filtered reference least mean square (MCFxLMS) algorithm often suffers from high computational complexity. This paper proposes a feedforward MVANC system that incorporates the multichannel adjoint least mean square (MCALMS) algorithm to overcome these limitations effectively. Computational analysis demonstrates the improvement of computational efficiency and numerical simulations exhibit comparable noise reduction performance at virtual locations compared to the conventional MCFxLMS algorithm. Additionally, the effects of varied tuning noises on system performance are also investigated, providing insightful findings on optimizing MVANC systems.

Global active noise control using SIMO near-field systems - Simulation and technical implementation

The technical implementation of a single-input multiple-output (SIMO) active near-field noise control (ANC) system designed for global low-frequency noise control is presented. The SIMO control strategy is motivated by noise control simulations based on a direct boundary element method (BEM) ansatz formulated in this paper. The suitability of the chosen BEM formulation and SIMO control strategy is accompanied and confirmed by extensive experiments and comparison to analytical calculations based on isotropic sources. The influence of parameters such as the positioning of the error microphone, the membrane velocity required for optimal global reduction, the effects of phase inaccuracies and the number of actuators on global reduction are investigated. The real-time control of the SIMO system is technically implemented on field programmable gate array (FPGA) hardware using a frequency domain block FxLMS algorithm. The iterative algorithm converges in less than 0.1 s for the examined frequency range from 100 Hz to 1000 Hz. Measurements using various circular SIMO array configurations showed reductions in sound power of up to 85\%, aligning closely with the numerical results.

Attention-based convolutional neural network for multi-channel active noise control

Multi-channel active noise control systems utilize multiple control units to cancel unwanted noise across different channels simultaneously. By employing adaptive filtering techniques, these systems can effectively reduce noise levels in complex environments, offering improved noise mitigation compared to single-channel active noise control approaches. The disadvantage of adaptive filtering in active noise control is its limited ability to accurately model nonlinearities present in real-world noise environments. In this work, we propose an attention-based convolutional neural network comprising convolutional, deconvolutional, and attention mechanism to approach multi-channel active noise control. The system simultaneously calculates multiple canceling signals to eliminate primary noises captured by error microphones. To evaluate the effectiveness of our proposed model, we conducted testing utilizing filtered white noise spanning various frequency bands. Experimental evaluations across various setups, including different loudspeaker and microphone configurations, as well as variations in secondary source positioning, are used to assess the performance of the proposed approach. Results demonstrate that the proposed attention-based convolutional neural network for multi-channel active noise control yields a noise reduction of 22.65 dB on wideband noise, while the conventional Filtered-x least mean square approach yields a noise reduction of 4.23 dB, indicating the effectiveness of the proposed approach.

Exploring the effects of phase delay on propeller noise: an experimental study in tandem configuration

Noise cancellation is a revolutionary control strategy, so effective that it has been integrated into high-end headphones. Implementing this approach on drones may seem challenging, but at the same time, it is highly appealing. The idea is to use some rotors, which constitute the main noise source, as a secondary pressure wave to eliminate the noise emitted by the entire propulsion system by inducing destructive interference among the individual sources. Implementing this kind of technology requires a significant understanding of the interaction mechanism of pressure waves emitted by the blades. The associated signature is not sinusoidal and exhibits strong directivity. This article will clearly demonstrate the potential of this approach by focusing attention not only on the effects but also on the nature of the interference mechanism. This finding is pivotal for the design of an active noise control system.

Analytical model of an active noise control system for performance analysis of adaptive algorithms in HVAC system

In real-world acoustic scenarios related to the problem with the low-frequency noise such as HVAC systems, the signal is a stochastic, non-stationary and time-varying process where the performance of the active noise control technique mainly depends on the characteristics of the adaptive filtering techniques. In this paper, a method for analytical modeling of active noise control of signals in MATLAB/Simulink is presented, in which the acquisition of audio signals is provided by using a National Instruments control and acquisition module. For the realization of the active noise control, an adaptive filtering system model for signal analysis has been developed, using three adaptive algorithms (LMS,RLS and NLMS). The analysis of the characteristics of each of the three investigated adaptive algorithms is performed according to four parameters: Mean Square Error (MSE), convergence speed, stability and robustness. The conclusions are presented through a comparative analysis of the results obtained from the implemented methodology. The individual characteristics and parameters that are observed for correct adaptation and optimal results of each of the adaptive filters are explained in detail.

A low-complexity parallel local remote microphone technology for multichannel narrowband active noise control systems

Narrowband active noise control (NANC) systems can effectively eliminate periodic disturbances at its error sensors, but sometimes the error sensors are inconvenient to be placed at target locations for a long period. Remote microphone technology (RMT) can tackle this problem by moving quiet zones to the target areas. Nevertheless, the RMT-NANC system has significantly higher computational complexity than conventional NANC systems, particularly for multichannel systems. This hinders their implementation in real-time systems with limited computational resources. The additional computational burden stems from multiple convolutions involving the estimated global high-order secondary paths and observation filters when estimating the virtual error signals. To address this problem, a low-complexity parallel local RMT is proposed based on the narrowband filtered-x least mean square (PLRMT-NFxLMS) algorithm in this paper. Using a two-step local modeling approach, multiple local modeling low-order filters for both secondary paths and observation filters are constructed during the training stage. In the subsequent control stage, virtual error signals are estimated in parallel for different frequency components using these filters instead of global modeling filters, thereby alleviating the substantial computational cost arising from convolutions between lengthy vectors. Moreover, a filtered-error structure, termed the PLRMT-NFeLMS algorithm, is introduced in the proposed algorithm to further reduce the computational complexity. A comprehensive analysis of computational complexity is provided to demonstrate the superiority of the two proposed algorithms. Extensive simulations and real-time experiments were conducted to validate the feasibility and practicability of these proposed methods.

An efficient low-delay polyphase implementation method for active noise control systems

When implementing active noise control systems on signal processing hardware, the time delay introduced by electronic components (especially components requiring additional lowpass filters or introducing fixed-sample-size delays) may adversely affect the noise control performance. One common approach to reducing this delay is to use a high sampling rate, but this increases the computation significantly when implementing the ANC filters in real time. In the current work, a polyphase-structure-based filter design method is developed for active noise control systems that can reduce the computation load for real-time filter implementation but do not introduce an additional time delay. Although the computation reduction capability of a polyphase filter structure is well known for multi-rate systems, the traditional use of such multi-rate systems requires additional anti-aliasing and reconstruction filters which introduces an additional time delay. Thus, in delay-sensitive applications, such as active noise control, this method was previously applied only on the filter adaption phase, instead of directly on the real-time filtering process. In this article, a filter decomposition method using the minimum-phase technique is proposed to decompose an ANC filter into two multiplicative causal filters both of which have lowpass frequency response shapes at high frequencies such that the polyphase structure can be applied directly to the two multiplicative causal control filters without introducing additional anti-aliasing and reconstruction filters. Results show that, compared with various traditional low sampling rate implementations, the proposed method can significantly improve the noise control performance. Compared with the non-polyphase high-sampling rate method, the real-time computations that increase with the sampling rate are improved from quadratically to linearly.

A time-domain study on the effects of noise source incidence on plenum window with active noise control

Active noise control (ANC) applied to the built environment on windows presents itself as a potential solution to reduce noise exposure for residents living near major noise sources since windows are typically the primary path for noise intrusion on a building's facade. The plenum window design seeks to address the dual dichotomy of ventilation and acoustic dilemma that plagues hot-humid and highly noise-polluted cities. In the present study, a time-domain approach is developed to investigate the effect of varying impinging angles of noise sources on the acoustic performance of a plenum window which can represent different storeys of the receiving end of noise exposure. Furthermore, the ANC performance of a control system affected by the varying noise sources is explored in this study. While most ANC studies have limited their scope of study to frequency domain investigations, the present study takes a time-domain approach to account for the causality of the ANC system concerning the primary noise source and secondary control source, which is thus more accurate to the behaviour of a real-time ANC controller. A total of 121 simulation studies were performed, and the findings show that the plenum window's PNC (passive noise control) is affected by the incident angle of a noise source based on its tonal frequency, which is pronounced near the aperture's cut-off frequency, faperture. However, the ANC performance due to varying incident angles of primary noise remains unaffected even at high frequencies with a standard deviation, σANC < 1 dB except for the 630 Hz tonal frequency, which coincides with faperture.

A Robust Filtered-x Least Mean Square Algorithm with Adjustable Parameters for Active Impulsive Noise Control

In active noise control (ANC) systems, the traditional filtered-x least mean square (FxLMS) algorithm has poor control effect on impulsive noise. To overcome this drawback, a robust cost function was designed in this paper by embedding the cost function of the FxLMS algorithm into the framework of hyperbolic tangent function; this paper thus proposes a robust filtered-x least hyperbolic tangent (FxLHT) algorithm in ANC systems. Moreover, the value of λ in the FxLHT algorithm greatly affects the robustness and convergence performance of the algorithm. Therefore, a variable λ-parameter was proposed to enhance the performance of the FxLHT algorithm. Simulation results show that in the active control of impulsive noise, compared with the FxLMS algorithm and other robust ANC algorithms, the proposed FxLHT algorithm and variable λ-parameter FxLHT algorithm not only exhibit good robustness and noise reduction performance but also have a better tracking ability.

A modified feedforward-feedback time-frequency domain hybrid algorithm for multichannel active road noise control system

Multi-channel Active Road Noise Control (ARNC) systems require numerous adaptive filters to reduce noise at multiple positions inside the vehicle. However, road noise conditions are more complex, and the correlation between the reference signals and the target noise is often poor. When the driving conditions become slightly complex, the correlation between the reference signal and the target noise is often poor. As a result, traditional road noise control algorithms applied to multi-channel ARNC systems often suffer from high computational cost and poor noise reduction performance in non-steady-state and high-speed driving conditions. To address this issue,a reference signal selection method based on the genetic algorithm (GA) is proposed to obtain a reference signals combination with stronger coherence. On this basis, we introduce the time–frequency domain structure into the forward-feedback hybrid algorithm and a multi-channel time–frequency domain hybrid algorithm with a novel variable step size method (VSS-TF-HANC) is proposed in this paper.The feedforward part adopts a time–frequency domain structure and introduces a novel variable step-size strategy to reduce computational complexity and improve noise reduction performance. The feedback part adopts a control system based on the Internal Model Control (IMC) architecture to attenuate the interior noise components that have a strong response but poor correlation with the reference signals. Simulation results demonstrate that the proposed algorithm achieves excellent control results under complex driving conditions, with significantly better noise reduction performance compared to the control algorithms that solely employ the feedforward structure, while also exhibiting low computational cost.