Conventional Active Noise Control Research Articles

Active noise control without tap length selection: a model order weighting method

Conventional active noise control systems typically rely on fixed tap lengths in control filters. Determining an appropriate tap length is challenging, even within a straightforward time-invariant environment. This challenge is exacerbated when essential system responses, such as primary noise characteristics or primary path responses, remain partially unknown and necessitate the use of an adaptive filter. A trade-off emerges between convergence rate and steady-state performance when selecting a fixed tap length in adaptive filters. In time-varying environments, the optimal tap length may even dynamically shift over time. Thus, prior attempts have been made to introduce variable tap length algorithms to dynamically adapt tap length in real time. However, the performance of variable tap length algorithms can be sensitive to the choice of additional parameters and noise level. This paper exploits a model order weighting approach by combining the outputs of filters of different tap lengths based on their predicted noise control performance. This proposed method demands minimal additional prior information compared to the variable tap length methods. The noise control performance, as demonstrated by specific examples of active noise control, is presented and analyzed.

Enhancing active noise control through stacked autoencoders: Training strategies, comparative analysis, and evaluation with practical setup

This work is motivated by the imperative to overcome the limitations of conventional active noise control (ANC) techniques. These limitations are rooted in linear systems like the least mean square algorithm when faced with nonlinear distortions in the acoustic environment and system electronics, such as loudspeakers. This work addresses the nonlinear ANC problem by conceptualizing ANC as a deep learning problem. A novel stacked autoencoder (SAE) model is proposed, which is trained to estimate the noise that should be played at a loudspeaker so that the noise received at an error microphone may be attenuated. ANC experiments are initially set up in an anechoic room to study the fundamental performance characteristics of the architecture without extraneous considerations of room impulse response. This is followed by performance analysis in a normal reflective room. Actual noise, comprising broadband and tonal noise, is recorded in both an anechoic room and a normal reflective room. The importance of these experiments in the design process lies in their ability to account for nonlinear effects in actual setups, surpassing the limitations of relying solely on models. Experiment outcomes demonstrate that the proposed SAE model yields a noise reduction (NR) of up to 26.44 dB for wideband noise and up to 35.74 dB for tonal noise types. Additionally, there is an improvement in the extent of the quiet zone provided by the proposed SAE for tonal noise cases as compared to the traditional filtered-x least mean square (FxLMS) technique.

A robust active noise control system based on an exponential hyperbolic cosine norm

The performance of conventional active noise control (ANC) systems degrades under non-Gaussian conditions. In the recent past, generalized filtered-x maximum correntropy criterion (FxGMCC) algorithm has been widely used to tackle such non-Gaussian noises. However, noise reduction performance of maximum correntropy criterion (MCC) based algorithms degrades due to the high steady-state misalignment. To overcome this limitation, a filtered-x exponential hyperbolic cosine adaptive filter (FxEHCAF) algorithm is proposed in this paper. Later, an affine combination technique has been applied and the corresponding affine FxEHCAF (A-FxEHCAF) algorithm has been developed here showing faster convergence rate in transient state along with low residual error in steady-state. Simulation study shows that the proposed FxEHCAF and A-FxEHCAF algorithms provide enhanced noise control performance over other existing algorithms.

Application of Active Impulsive Noise Control (AINC) for Excavator Cabin Using Advanced Convex-Combined Normalized step size Algorithm and Verification of Robustness

Conventional Active Noise Control (ANC) algorithms, such as Filtered-x least mean square (FxLMS), is not suitable for Active Impulse Noise Control (AINC). Because it lacks robustness to the impulsive sample input, it is unstable and diverges very easily. Therefore, AINC algorithm must be robust to impulse. The heavy equipment such as excavators often occur frequent impulse noise during operations. Also, in working environment of operating excavator, impulsive noise is prone to occurred by other heavy equipment. Therefore, it is necessary to examine the AINC algorithm that is suitable for excavators and its robustness. In this study, Convex Combined Step Size (CCSS), which is simulated better effects in AINC than conventional ANC algorithms, is tried to AINC of excavator. In addition, the noise reduction performance and robustness to undesirable sound were verified in the simulated experiment environment of the excavator cabin.

Convex combination of online secondary path modeling and improved cuckoo search for active noise control

This paper proposes an active noise control (ANC) algorithm based on convex combination of the online secondary path modeling (OSPM) and improved cuckoo search (ICS) algorithms. The ICS algorithm does not request the secondary path model to optimize the control filter. Therefore, the ICS algorithm is theoretically not affected by a varying secondary path, but its convergence is very slow. In contrast, the OSPM algorithm achieves faster convergence but is vulnerable to a sudden change in the secondary path. The proposed ANC algorithm combines the merits of both the OSPM and ICS algorithms by convex combination. Simulation results show that the proposed ANC algorithm can achieve better performance than the conventional ANC algorithm especially when the secondary path changes drastically.

Experimental evaluation of feedforward active noise control system with optical laser microphone and proportional-integral-differential filter

In this paper, we propose a feedforward active noise control (ANC) system with an optical laser microphone utilizing a proportional-integral-differential (PID) filter. The feedforward ANC system with the optical laser microphone has been proposed to relax the causality constraint. This system adopts a first-order differentiator to modify the velocity picked up by the optical laser microphone. This modification achieves the improvement of the coherence between the velocity as the reference signal and the unwanted noise as the sound pressure. However, the frequency response of the first-order differentiator is similar to the high-pass filter and attenuates the reference signal at low frequencies. This causes the degradation of the noise reduction performance of the conventional ANC system. To solve this problem, the proposed ANC system adopts the PID filter instead of the first-order differentiator. By adjusting the coefficients of the PID filter, the proposed system avoids the attenuation of the reference signal at low frequencies. Then, the noise reduction performance can be improved. Experimental results show that the proposed ANC system reduces the unwanted noise compared to the conventional ANC system and relaxes the causality constraint compared to the basic ANC system in the real-time operation.

Evaluation of virtual sensing with spherical array surrounding head for directional noise

Active noise control (ANC) is expected to be applicable to public transportation modalities such as airplanes and trains. In these application systems, passengers may need to avoid wearing devices such as earphones and ear microphones. However, in conventional ANC systems, it is necessary to locate a physical microphone at the ear positions as an error sensor. To overcome this problem, virtual sensing techniques have been studied for estimating sound pressures at ear positions using remote microphones. In this study, we investigated the performance of a virtual sensing method for interpolating the sound pressures at ear positions using a spherical microphone array surrounding the head, based on a spherical harmonic expansion. The experiments were conducted using a rigid sphere instead of a head and dodecahedral spherical microphone array surrounding the sphere. The estimation errors were evaluated for various noise directions at low frequencies (100, 300, 500, and 700 Hz). The experimental results showed that the estimation errors were less than -20 dB for almost all conditions below 500 Hz. By comparison with other virtual sensing methods using a few microphones in the vicinity of the ear positions, we confirmed that our proposed method was effective for any noise direction.

A hat-shape error microphone array for user-friendly spatial active noise control

Spatial active noise control (ANC) has been developed in the past decades to reduce unwanted acoustic noise over a desired region by generating an anti-noise field with secondary loudspeakers. Unlike conventional multi-channel ANC systems, the integration of spherical harmonic-based sound field analysis allows the ANC system to reduce noise over a continuous region surrounding the user's head rather than discrete points. However, the use of a spherical error microphone array on the boundary of the region for spherical harmonic analysis poses a limitation as it obstructs user access to the controlled area. In this work, we introduce a hat-shaped error microphone array design which can be placed above the listener's head. The proposed error microphone array can achieve spherical harmonic-based residual noise field analysis and is more user-friendly, allowing unrestricted user head movement. We construct the hat-shaped error microphone array and present the results of ANC experiments conducted using the array.

Time domain virtual sensing method based on a rigid-sphere transfer function for active noise control headrests

Active noise control (ANC) is expected to be used in public transportation such as airplanes and trains. While using these applications, passengers prefer not to wear earphones or ear microphones, if possible, to reduce ear strain. However, conventional ANC systems require a physical microphone, which acts as an error sensor, at the ear position. To address this issue, a virtual sensing technique has been studied that uses a microphone mounted on a seat’s headrest to estimate the sound pressure at the ear position. In this study, we propose a virtual sensing technique that interpolates the sound pressure at the ear position using a spherical microphone array surrounding the head. This method improves robustness to variations in noise arrival direction using spherical harmonic coefficients without directional dependence. In general, the interpolation formula using spherical harmonic expansion is obtained in the frequency domain. By contrast, the proposed method designs the interpolation process as an IIR filter by considering the minimum phase and estimates the noise signal directly in the time domain, achieving the real-time processing required for ANC. The effectiveness of the proposed method in reducing noise was investigated for various noise arrival directions.

A low-complexity multi-channel active noise control system using local secondary path estimation and clustered control strategy for vehicle interior engine noise

Conventional multi-channel active noise control (ANC) system based on the adaptive notch filtered-x least mean square algorithm is typically used for active control of vehicle interior engine noise with prominent harmonic characteristics. However, due to the large estimated secondary path length and the centralized control strategy, the system has high computational complexity and fragile stability. To alleviate this problem, an efficient multi-channel ANC system using the local secondary path (LSP) estimation and clustered control strategy is proposed in this paper. Based on the modified LSP estimation method, the system may utilize a set of low-order but accurate LSP models to simplify the convolution operations of reference filtering. Besides, the proposed clustered control strategy combines the advantages of centralized and decentralized control strategies, and thus the system not only enjoys good noise attenuation performance and stability, but also its computational burden is further greatly reduced. Numerical simulations are performed to evaluate the noise attenuation performance and frequency mismatch effect of the proposed multi-channel ANC system. In addition, a series of real vehicle ANC experiments are conducted. The results show that, under stationary work conditions, the interior overall, 2nd order, 4th order, 6th order engine noises may be attenuated by 12.91 dB(A), 32.98 dB(A), 15.34 dB(A) and 8.74 dB(A), respectively, and under nonstationary work conditions, the maximum overall noise reductions at the four error microphones may reach 6.40 dB(A), 6.14 dB(A), 12.48 dB(A) and 11.07 dB(A), respectively. This fully confirms the effectiveness of the proposed multi-channel ANC system in real-world applications.

Maximum versoria criterion applied to hybrid active noise control algorithm for the impulsive noise

In the conventional hybrid active noise control (CHANC) algorithm, the filtered-x least mean square (FXLMS) algorithm is used in both feedforward and feedback structures. However, the FXLMS algorithm does not process the reference signal and the error signal before updating the weight vector, which leads to the inadequate robustness of the CHANC algorithm in the impulsive noise environment. To solve this problem, the maximum versoria criterion is incorporated into the HANC (MVC-HANC) algorithm in this paper. Firstly, the MVC-HANC algorithm employs a novel nonlinear function to compress the error signal. Secondly, the proposed algorithm uses the modified sigmoid function to constrain the reference signal. To further improve the noise attenuation performance, the MVC-HANC algorithm uses a novel exponential function to adjust the step-size adaptively. Simulation and experiment results demonstrate that the proposed algorithm has the better attenuation performance than the conventional HANC algorithm.

A Robust Adaptive Filter for Diffusion Strategy-based Distributed Active Noise Control

Active noise reduction in the existential of impulsive noise poses a serious challenge in a distributed scenario. The conventional centralized multichannel active noise control (MANC) and diffusion filtered-x least mean square (F-xLMS) based distributed active noise control (DANC) systems exhibit deteriorated performance in the existence of impulse noise and outliers. In this regard, a diffusion least logarithmic hyperbolic cosine function (D-Llncosh) based algorithm for DANC system is developed with the goal of mitigating noise in such an environment. It utilizes natural logarithmic of hyperbolic cosine function integrating the mean square error (MSE) and mean absolute error (MAE) as the cost function depending upon the parameter value. The weight update rule of the introduced D-Llncosh-based DANC system is hereby derived. The convergence analysis of the suggested method is also carried out. The proposed system is validated for various types of impulsive noise under different scenarios. The extensive simulation results assert the efficacy of the posited DANC system in comparison with the existing techniques. The suggested method achieves quicker convergence and approximately a 1–5 dB improvement is the noise reduction for different noise scenarios as evident from the obtained results.

Suboptimal controller design of global active noise control system for various acoustic environments

Conventional active noise control (ANC) systems in enclosed spaces are not easy to implement experimentally because they require a large number of microphones to measure sound pressure in global areas. Even if such systems are possible, if there are any changes in the locations of noise sources or surrounding objects, or if ANC system moves to another enclosed space, an expensive and time-consuming experimental calibration is again required. Implementation of global ANC in enclosed spaces is thus difficult. Therefore, we designed a global ANC system that can be used in various acoustic environments. The main idea involves suboptimal open-loop controller design in the free field. By using an open-loop controller, a controller calibrated once can be used in various acoustic environments. A controller designed in the free field derive a suboptimal solution without bias toward a specific acoustic environment. For controller design in the free field, we propose an experimental calibration approach in which the arrangement and the number of control speakers and microphones are determined by the frequency range and radiation pattern of the noise source. We conducted simulations and experiments to show that the designed controller in the free field is sufficiently effective in other enclosed spaces.

Head-Mounted Multi-Channel Feedforward Active Noise Control System for Reducing Noise Arriving From Various Directions

A head-mounted active noise control (ANC) system based on the single-channel ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\times 1 \times 1$ </tex-math></inline-formula> ) feedforward control has been studied as a noise reduction method in complicated noise environments such as factories in which various machines are located that generate unwanted noises. However, it is difficult to reduce the noise arriving from various directions because the causality constraint cannot be satisfied. To solve this problem, we developed a head-mounted ANC system based on the multi-channel ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times 1 \times 1$ </tex-math></inline-formula> ) feedforward control. This ANC system has two reference microphones that satisfy the causality constraint. Therefore, the proposed head-mounted ANC system achieves the expansion of noise reducible range of 0-100° compared to that of the conventional head-mounted ANC with the range of 0-60°. In this paper, we examine the effectiveness of the newly developed head-mounted ANC system. In the experiments, we evaluate the effectiveness of placing two reference microphones on noise reduction performance in the case of various arrival direction of the unwanted noise. Experimental results demonstrate that the proposed system increases the noise reducible range and improves the noise reduction performance for two noise sources. In addition, we theoretically prove the effectiveness of the proposed system.

차량 능동 소음 제어 시스템에서 제어 위치 선택에 따른 소음 저감 성능에 관한 예비 평가

This study uses a modified cost function with weighted error signal channels to demonstrate that the vehicle active noise control system can focus control efforts on one specific seat. In a conventional active noise control system for road noise suppression inside a passenger car, the control algorithm targets the attenuation of all seats, hence affecting the maximum performance that can be obtained when applied to a single seat. To address this limitation, we employed weighting factors in the error signal to control the selected seat positions only. A practical active noise control system was implemented in a large sedan to investigate the effect of the weighting factor on the noise attenuation performance. Experimental results showed that the error weighting factor strategy increased attenuation of the target seats by up to 1.15 dB more than in the case of the entire seat control.

Horizontal Active Noise Control Based on Wave Field Reproduction Using a Single Circular Array in 3D Space

In this paper, we propose horizontal active noise control (ANC) using two-dimensional wave field information alone. By reducing the control space to a horizontal plane, the number of microphones and speakers was considerably reduced compared with ANC systems using three-dimensional wave field information. The radii of the reference, microphone, and loudspeaker array were determined based on the wave field reproduction error. Accordingly, the simulation and experimental results of the proposed ANC system were presented based on the use of five microphones and loudspeakers using conventional ANC algorithms. Overall, an average noise reduction of 20 dB was observed inside the microphone array with a radius of 0.5 m for tonal noise at 200 Hz. This performance is acceptable with a drastically reduced number of microphones and speakers. The findings of this study, along with further research conducted in a reverberant room, represent a significant contribution to global ANC commercialization.

Multi-channel wireless hybrid active noise control with fixed-adaptive control selection

Active noise control (ANC) is now a must-have feature in various headphones to attenuate unwanted external noise leaked into the headphones. The conventional ANC headphone typically uses an earcup-mounted reference microphone to pick up the reference signal and send it to the chip’s control filter, which generates anti-noise inside the earcup to destructively interfere with the leaky noise signal. Previously, a novel type of wireless feedforward ANC structure was devised to achieve greater noise reduction by acquiring a higher reference-to-interference ratio. To further reduce computational complexity and relax causality constraints over traditional ANC algorithms, a modification to this structure is proposed that employs a hybrid feedforward–feedback structure in conjunction with a fixed-adaptive control selection (FAS). Furthermore, numerical simulations and real-time experiments are performed in this paper to validate the effectiveness of the wireless hybrid ANC with the FAS technique.

An Active Indoor Noise Control System Based on CS Algorithm

Environmental noise causes enormous harm to human health. This research suggests an active noise control (ANC) system based on the cuckoo search (CS) algorithm to reduce indoor noise pollution. The indoor environment’s ANC is more complicated than the conventional linear ANC system used in cars, aircraft, and other confined spaces. The suggested system architecture considers noise from many directions in order to reduce the impact of external noise on persons within a given environment. The effective search strategy of the CS algorithm enhances the approach of updating filter coefficients based on the LMS/NLMS algorithm in the conventional ANC systems. The results from the simulations demonstrate that the suggested strategy successfully validates the hypothesis and provides significant noise reduction. Additionally, we developed the system’s hardware, which is based on a digital signal processor (DSP). The experimental results show that the proposed technology could perform well with respect to ANC.

Convex combination of the FxAPV algorithm for active impulsive noise control

Conventional active noise control systems suffer from performance degradation when exposed to impulsive interference. Taking the maximum Versoria criterion (MVC) function as the cost function, an MVC-based filtered-x affine projection algorithm is derived by solving the inequality constraint problem. The algorithm has a fast convergence speed when dealing with active impulsive noise control. Subsequently, a convex combinatorial structure is proposed. The stability and computational complexity of the algorithms are analyzed. A genetic algorithm is used to select the appropriate parameters for different algorithms. The simulation results show that the proposed algorithm has a faster convergence rate in addition to a greater average noise reduction. Lastly, the effectiveness of the proposed algorithms is verified experimentally.

A distributed FxLMS algorithm for narrowband active noise control and its convergence analysis

A conventional multichannel narrowband active noise control (MNANC) system can effectively suppress low-frequency periodic noise. However, a large number of secondary sources and error sensors are required in the system, which leads to high computational complexity. To address this problem, a diffusion narrowband filtered-x least-mean-square (DNFxLMS) algorithm is proposed based on distributed acoustic sensor networks. The computational burden is dispersed among the nodes over the networks. A comprehensive statistical analysis of the algorithm is conducted to better understand its convergence properties. Specifically, we investigate the convergence behavior of the weight-error vector in the mean and mean-square sense. Based on this analysis, the stability bound of the system is discussed. The expressions for steady-state mean-square deviation (MSD) and excess mean-square error (EMSE) are derived. Extensive computer simulations are conducted to verify the theoretical analysis and to evaluate the usefulness of the DNFxLMS algorithm.