Noise Control Algorithm Research Articles

Optimal ANC System Arrangement Based on Complete System Analyses Applying COSMOL Multiphysics and Matlab

In real active noise control system implementation, the arrangement of secondary sources and error microphones have significant effect on the performance of the system. Analytical and experimental ways are usually combined to determine the best system layout. In this paper, we use COSMOL Multiphysics to accurately model the acoustic environment in enclosures with the real measured dimensions and parameters. Matlab is adopted to simulate the basic active noise control algorithms. The combined simulation results are used to decide the optimal system layout of the real ANC system. Experiments are conducted on a real ANC system with EVAL-21489-EZLITE from ADI to validate the analyzed and simulation results.

Active Noise Control Algorithm with Saturated Actuator

Active noise control system has received its attention in various technical field such as headphone, motor vehicle, etc. Meanwhile, filtered-x least mean square (FxLMS) algorithm is conventional linear algorithm used in active noise control system. It assumes that acoustic path from the noise source and control source to target area are linear. However, in actual system, the secondary path including a D/A converter, an amplifier, and an actuator may exhibits nonlinear distortion like saturation effects. To cope with this nonlinear effects, functional link artificial neural network (FLANN) has been proposed. FLANN uses nonlinear function expansion filter with FxLMS based control algorithm to control the nonlinear effect. In this paper, noise reduction performance and convergence speed are improved by modifying the conventional FLANN algorithm by decoupling the linear and nonlinear part of noise signal.

Simulation of active noise control using transform domain adaptive filter algorithms linked with the inhomogeneous Helmholtz equation

An iterative approach is presented that couples the inhomogeneous Helmholtz equation with transform domain active noise control algorithms. The Boundary Element Method is utilized to provide solutions of the inhomogeneous Helmholtz equations for three dimensional pressure wave fields. The source terms in the inhomogeneous Helmholtz equations are adapted iteratively by the algorithm to achieve convergence in the noise cancelling process. The approach can be used to study the impact of active noise control systems based on transform domain algorithms on the wavefield for specific application environments. Additionally, this approach allows for numerical frequency-dependent performance evaluation of multichannel active noise control systems even in acoustically challenging environments. Ill-posed control problems can be identified. The required calculation steps for the integration of the method into existing numerical simulation environments are presented step by step. The study is supplemented by numerical experiments and convergence studies that show good agreement with acoustic theory and analytical results reported in literature.

Optimal parametric design of delayless subband active noise control system based on genetic algorithm optimization

Active noise control systems are generally application-specific, and an appropriate algorithm with an optimal configuration is desirable in the first stage of active noise control system design and deployment. This study presents a design of the subband active noise control system with optimal parameters for a practical broadband active noise control. Although the delayless subband active noise control has gained wide attention for broadband noise cancellation, an optimal design remains a challenge because of the complex interplay between multiple factors such as spectral leakage, delay and weight stacking distortion subject to a number of configurable parameters, and weight stacking method. The configurable parameters can hardly be optimized independently because the active noise control performance depends on the combined configuration. A simple near black box active noise control algorithm optimization model is thus established by incorporating the genetic algorithm optimization into the parametric design of the delayless subband algorithm. The automated process does not require an understanding of the performance characteristics for different parameters. The significance of applying the automated genetic algorithm optimization to the complex multiparameter nonlinear active noise control design is revealed by numerical simulations, particularly for the multichannel low-frequency broadband active noise control system configured with the delayless subband algorithms. This provides a way for the optimal parametric design of subband active noise control before being used in a practical complex scenario.

Improved active noise control algorithm based on the convex combination method

Noise can be harmful in certain situations, which leads to the need of controlling it. In general, a particular method of active noise control basically consists of creating a destructive overlap between noise and a reference signal (both with the same frequency and amplitude but with an opposite phase). Different algorithms can be used for this purpose, each of them having distinct features, like convergence, velocity, and steady state attenuation level. The convex combination method for active noise control (C-ANC) has been used successfully as it combines different characteristics of most algorithms found in literature. This method combines two distinct active noise control algorithms into a single one. For instance, it can use algorithms such as FXLMF, which has good attenuation velocity, and the FXLMS algorithm, which has a significant attenuation level. This work proposes a modified convex combination algorithm, named MC-FXLMS-F, in order to maximize the overall attenuation level. Modification of the convex combination method was implemented using the FXLMS-F algorithm. The new proposed algorithm was compared to others found in literature, such as the FXLMS, FXLMS-F, and C-FXLMS-F algorithms. The modified convex combination algorithm (MC-FXLMS-F) showed improvement in both convergence speed and attenuation level. It achieved the best performance among the most popular existing algorithms.

Modification of frequency-domain active noise control algorithm without secondary path modeling.

The phase-shifting based active noise control (ANC) algorithm without secondary path modeling is appealing due to its simple implementation and high computational efficiency. However, the algorithm suffers from the influence of an implicit circular convolution caused by directly applying the most appropriate phase shift to the reference signal in the frequency domain. The implicit circular convolution results in a poor linear relationship between the phase-shifted reference signal and the error signal, which considerably degrades the convergence performance of the ANC system. In this paper, two methods are proposed to deal with this problem. In the first method, the circular wraparound artifacts are removed by the time-domain constraining operation. In the second method, a noncausal phase-shifting filter is used to regularize the phase of the reference signal. Compared with the standard frequency-domain implementation, the two proposed algorithms both eliminate the influence caused by the wraparound artifacts of the circular convolution and improve the linear relationship between the phase-shifted reference signal and the error signal, leading to a higher level of noise attenuation with a tolerable increase in computational complexity. Simulation results validate the better noise attenuation ability of the proposed algorithms.

FxRLS Algorithms Based Active Control of Impulsive Noise With Online Secondary Path Modeling

Active noise control algorithms undergo stability problems in the presence of impulsive noise. This paper investigates such algorithms with online secondary path modeling for impulsive noise and varying acoustic paths. The paper presents three methods for active noise control, along with improved online secondary path modeling. Firstly, the filtered x recursive least square algorithm is applied for both active noise control and online secondary path modeling. This method gave faster convergence, improved stability, and modeling accuracy as compared to existing ones. The filtered x recursive least square algorithm is not robust for abruptly changing acoustic paths. To overcome this problem another method that uses modified gain filtered x recursive least square algorithm for active noise control is presented. Furthermore, it is observed that modified gain filtered x recursive least square achieves the desired performance with overheads of increased complexity. Thus, a hybrid method is proposed which has less computational complexity than the rest methods with no compromise on active noise control system performance.

Application of probabilistic assessment for optimal prediction in active noise control algorithms

Abstract This study explores a modified active noise control (ANC) system using a Bayesian inference approach as a pre-processing method. The key aspect of low-frequency noise attenuation is investigated with the existing control algorithms, the conventional filtered-x least mean square (FxLMS) algorithm and a new convex structure via an FxLMS/F algorithm (C-FxLMS/F), that combine Bayesian inference with a dynamic linear model (DLM). The combination of a Bayesian approach and a DLM comprises the statistic strategy and a descriptive time series, which is conductive to raw signal pre-processing and concurrently generating a predicted signal as a reference signal. For signal processing, pretreatment enables the determination of the noise characteristics of the operating machine and its feedback to the control system. This is an important input to enable the time domain control algorithm to prevent environmental disturbance and time-delay effects. In addition, the use of active control theory mainly relies on the response time of secondary source generation. The predicted signals based on prior observational information and Bayesian inference afford an alternative to the normal costs of the secondary path, such as those associated with electro-acoustic signal conversion and computation efforts in the control algorithm. In this work, the combination of a Bayesian approach and an FxLMS algorithm is studied via a case study. To explore more applicability, the combination of a C-FxLMS/F algorithm with Bayesian inference is also investigated, and a convergence analysis is presented. The in-situ measurement data obtained from a construction site acoustic apparatus is used for analysis. The simulation results are presented via two illustrative cases. In addition, a comparison for three different signal forms under the effect of Bayesian inference is also discussed. It is found that a Bayesian inference approach based on DLM is workable in the ANC system, and the convergence performance is superior to that of an ANC system without Bayesian inference. This suggests that to implement such a system for signal control, it is better to enhance the final system performance in the time-domain field of ANC algorithms. This pre-processing system based on a characteristic strategy and having a low computational loss is needed not only to reduce the time-delay compromise, but also to prevent the sudden disturbance of the reference signal.

Using empirical wavelet transform to speed up selective filtered active noise control system.

The gradual adaptation and possibility of divergence hinder the active noise control system from being applied to a wider range of applications. Selective active noise control has been proposed to rapidly reduce noise by selecting a pre-trained control filter for different primary noise detected without an error microphone. For stationary noise, considerable noise reduction performance with a short selection period is obtained. For non-stationary noise, more restrictive requirements are imposed on instant convergence, as it leads to faster tracking and better noise reduction performance. To speed up a selective filtered active noise control system, empirical wavelet transform is introduced here to accurately and instantaneously extract the frequency information of primary noise. The boundary of the first intrinsic mode function of random noises is extracted as the instant signal feature. Primary noise is attenuated immediately by picking the optimal pre-trained control filter labeled by the nearest boundary. The storage requirement for a pre-trained control filter library is reduced. Instant control is obtained, and the instability caused by output saturation is overcome. With more concentrated energy distribution, better noise reduction performance is achieved by the proposed algorithm compared to conventional and selective active noise control algorithms. Simulation results validate these advantages of the proposed algorithm.

A new feedforward hybrid active control system for attenuating multi-frequency noise with bursty interference

Abstract Noise from rotating machinery in an enclosed space exhibits both narrowband and broadband characteristics. Hybrid active noise control (HANC) algorithms are usually used in this case to cancel both kinds of noise. However, sometimes also bursty noise occurs. In a mechanical system, for example, it may occur in every cycle if the gear wheel faces are damaged. Bursty noise has high amplitudes and short duration, and this may deteriorate the narrowband attenuation performance of the HANC method. To address this problem, a feedforward HANC algorithm with a sinusoidal residual error structure (HANC-SRE) is proposed in this paper. By applying the sinusoidal residual error (SRE) subsystem, the proposed HANC-SRE algorithm can decrease the error interference between different narrowband adaptive filters for different frequencies. The structure of the traditional HANC is then redesigned to use the broadband filter as the primary noise absorber and the narrowband filters as secondary ones to achieve better performance for bursty noise. Moreover, we also introduce a variable step-size updating method to improve the tracking performance of the system. Theoretical analysis of convergence conditions and instantaneous residual output error is performed to evaluate the performance of the HANC-SRE algorithm. Experimental results demonstrate that HANC-SRE achieves better tracking performance while also being more robust than conventional HANC methods.

Adaptive Active Vehicle Interior Noise Control Algorithm Based on Nonlinear Signal Reconstruction

In this study, to reduce secondary sound source pollution in the reference signal of active noise control (ANC), a novel ANC algorithm, based on signal reconstruction, is proposed for vehicle interior noise. This algorithm combines the processes of ear-sides noise reconstruction and ANC. First, to reduce non-stationarity and nonlinearity, multi-source noise signals outside the vehicle are decomposed into a finite number of intrinsic mode function (IMF) components by empirical mode decomposition (EMD). Second, the IMFs are reconstructed by the energy-extreme division method into three components: high-frequency, intermediate-frequency and low-frequency. The radial basis function neural network (RBFNN) parameters are adjusted by the proportions of the components. Model training is performed to obtain the high-precision EMD–RBFNN reconstruction model (EMD–NNRM). The reconstructed noise signal is used as the reference signal of the variable step-size least mean square (VSS-LMS) algorithm, to control the passenger ear-sides noise. The effectiveness of the EMD–NNRM is validated using four noise signals from the outside of a vehicle. The interior noise of a high-speed vehicle is processed by the proposed algorithm and the traditional VSS-LMS algorithm for comparison. The reconstruction results show that the mean absolute error is improved by 77.64% compared with the back propagation neural network reconstruction model. Reconstructed passenger ear-sides noise can be utilized for ANC. The active control results suggest that the proposed algorithm can not only effectively suppress the interior noise but can also avoid pollution from secondary sound sources.

On FxLMS Scheme for Active Noise Control at Remote Location

The conventional adaptive noise control algorithms create a zone of quiet at the location of an error microphone. Several virtual active noise control (ANC) algorithms have been suggested in the literature to introduce a more flexible positioning of the zone of quiet. The main objective of the proposed ANC system in our research is to minimize the noise disturbance on human hearing by considering the auditory system characteristics. We have introduced the human perception of acoustic disturbances in our ANC modeling. This paper presents a novel approach based on a psychoacoustically enabled remote ANC algorithm to minimize the acoustic noise at a zone away from the error microphone location. This approach depends on modeling the transfer function between the error microphone and the remote zone of quiet. The developed model has been implemented using an FPGA real-time module, and the performance is validated at four different remote locations. Noise-weighting filters are incorporated to observe the psychoacoustic characteristics of the proposed model. In the experimental setup, the acoustic noise at the physical and remote locations are monitored to quantify the noise reduction characteristics of the proposed algorithm. The simulation and experimental results show a noise reduction of 15-18 dBs has been achieved, which is an average improvement of 5-8 dBs over the standard ANC model.

Efficient Narrowband Noise Cancellation System Using Adaptive Line Enhancer

Rotating machines such as motors, generate noise at the fundamental frequency and its harmonics. The narrowband active noise control (NANC) algorithm is widely used to cancel such noise. Traditional feedforward NANC systems use non-acoustic sensors to measure rotation speeds, and then a bank of signal generators produce synchronized tonal signals as the reference signals according to the fundamental frequency of the undesired noise. However, the non-acoustic sensors such as encoders usually cost a lot and are not reliable. This study proposes using the feedback ANC structure and incorporates with several adaptive line enhancers (ALEs) to generate the reference signals. Without using non-acoustic sensors, the proposed system only updates the center frequency of the first ALE to track the fundamental frequency change, which greatly reduces complexity of the proposed ANC system. The frequency mismatch (FM) problem is overcome by properly setting the bandwidths of the ALE. Performance were verified by using both simulations and real-time experiments.

Feedforward Selective Fixed-filter Active Noise Control: Algorithm and Implementation

Conventional real-time active noise control (ANC) usually employs the adaptive filtered-x least mean square (FxLMS) algorithm to approach optimum coefficients for the control filter. However, lengthy training is usually required, and the perceived noise reduction is not immediately realized. Motivated by the practical implementation, we propose a selective fixed-filter active noise control (SFANC) algorithm, which selects a pretrained control filter to attenuate the detected primary noise rapidly. On top of improved robustness, the complexity analysis reveals that SFANC appears to be more efficient. The SFANC algorithm chooses the most suitable control filter based on the frequency-band-match approach implemented in a partitioned frequency-domain filter. Through simulations, SFANC is shown to exhibit a satisfactory response time and steady-state noise reduction performance, even for time-varying noise and real non-stationary disturbance.

A narrowband active noise control algorithm considering the harmonic distortion of the loudspeaker

Electrodynamic loudspeakers are the main actuators of the active noise control system, and their harmonic distortion has a detrimental effect on the noise reduction of the system. To improve the performance, this paper proposes a novel narrowband active noise control algorithm with compensating the nonlinearity of the loudspeaker. In the proposed algorithm, the parameters of the controller are obtained by iteration through the filtered-x least mean square algorithm. Meanwhile, they are adjusted in real-time by establishing the online inverse model of the loudspeaker using the Volterra expansion. The simulation experiments for the typical loudspeaker model show that the proposed algorithm can dramatically improve noise reduction compared to the conventional algorithm.

A combined bilateral and binaural active noise control algorithm for closed-back headphones

Abstract Commercial noise-cancelation headphones are based on active noise control (ANC) algorithm. However, all existing ANC headphones are based on bilateral ANC approach, where two independent monaural ANC systems are used respectively for the left and the right ear cups. The performance of bilateral ANC approach strictly depends on the number of noise sources, direction of noise sources and noise types. Its performance decreases when the number of noise sources increases. Human’s hearing system is binaural in nature and noise control can take advantage of binaural processing to further enhance noise reduction compared to conventional bilateral ANC headphones. In this paper, we first propose a binaural ANC algorithm to evaluate its performance over the bilateral ANC algorithm and subsequently, modify into a combined bilateral-binaural ANC (CBBANC) algorithm for headphones in order to improve noise reduction performance for different cases when there are more than one noise sources and all noise sources are situated at different locations and for diffuse field noise. Experimentation results show that the combined binaural-bilateral ANC has better performance in all our tests compared to the conventional bilateral ANC headphones.

A narrowband active noise control system with reference synthesis

SummaryMost active noise control (ANC) algorithms require a model of the secondary path (SP). If there are large changes in the SP, online SP modeling algorithms may not be able to follow the changes quickly enough to prevent divergence. In this work, the mirror‐MFxLMS (MMFxLMS) is adapted to narrowband ANC, and the resulting algorithm is analyzed. The MMFxLMS is an algorithm similar to the MFxLMS and FxLMS but has online SP modeling; it is stable for any SP estimate and does not require auxiliary noise. In addition, a technique to alternate between MFxLMS and MMFxLMS is proposed, resulting in the M3FxLMS algorithm, which does not sacrifice performance and assures stability. However, the M3FxLMS requires a reference signal that is not available in feedback ANC. Feedback ANC is important in many practical cases, for instance, in ANC headphones. To solve this problem, a technique is proposed to synthesize a reference formed by a sum of sinusoids using measurements from the error sensor signal. This technique uses the fast Fourier transform to obtain estimates of the harmonic frequencies of the periodic noise components, and then refines and tracks these using a phase‐locked loop (PLL). The resulting algorithm enables a stable ANC system, even under large and sudden changes in the SP. The proposed PLL is also new and has a lock‐in range equal to the full band of the system.

Active suppression of microphonics detuning in high QL cavities

Operation of superconducting radio frequency (SRF) cavities with high loaded quality factors is becoming increasingly preferred for applications which involve low beam loading including energy recovery linacs (ERL). Vibration induced microphonics detuning poses a major operational bottleneck in these low bandwidth systems, adversely affecting field stability. Besides passive measures of mitigating the vibration sources, modern SRF cavities are also attached to fast tuners incorporating piezoelectric actuators. We demonstrate the narrow band active noise control algorithm for realizing active resonance control and propose a modification based on the least mean square approach to adaptively tune the control parameters and study its stability and performance. We discuss our experience of using passive mitigation techniques while commissioning the main linac cryomodule of the Cornell-BNL ERL test accelerator and report a net reduction in peak detuning by more than a factor of 2 in its unstiffened cavities. Finally, we demonstrate stable performance of our resonance control system with consistent reduction of peak microphonics detuning by almost a factor of 2 on multiple cavities.

Integrated psychoacoustic active noise control and masking

Abstract The active noise control (ANC) algorithms are widely applied in noise-canceling headphones. However, the disadvantage of conventional ANC algorithms is the high computational cost from high order finite-impulse-response filter, which also leads to slower convergence. Subband ANC algorithms, which are based on the subband adaptive filter, are used to reduce computational complexity and to increase convergence rate. Moreover, noise cancellation performance decreases at the higher frequency due to the decreasing control zone. In headphones based application, perceptual quality of noise attenuation and target audio signal are important factors, and ANC algorithm must be designed to account for the psychoacoustic parameters. In this paper, we propose a new noise reduction approach in order to solve some existing ANC headphones problems, especially high-frequency noise reduction and perceptual quality of the playback sound. The proposed algorithm is a new approach of integrated psychoacoustic subband ANC and psychoacoustic masking. Psychoacoustic subband ANC achieves lesser computational cost than conventional subband ANC by using the psychoacoustic model to attenuate only audible noise components during music playback in the headphones. Proposed new integrated system is efficient for broadband noise attenuation. Psychoacoustic ANC reduces noise in the most sensitive frequency range for human’s hearing system, 0–4000 Hz, and masking operates in order to mask out residual and high-frequency noise. In additional, proposed noise reduction system reduces only audible noise components. The integrated ANC-masking system applied psychoacoustic analysis without producing artificial distortions to the music signal. Objective and subjective perceptual tests are evaluated for the proposed ANC-masking system with other subband ANC and masking algorithms. Results show the advantage of the proposed integrated system in the terms of the perceptual sound quality and high-frequency noise reduction.

Feed-forward active noise control system using microphone array

Feedforward active noise control &#x0028 ANC &#x0029 system are widely used to reduce the wide-band noise in different application. In feedforward ANC systems, when the noise source is unknown, the misplacement of the reference microphone may violate the causality constraint. We present a performance analysis of the feedforward ANC system under a noncausal condition. The ANC system performance degrades when the degree of noncausality increases. This research applies the microphone array technique to feedforward ANC systems to solve the unknown noise source problem. The generalized cross-correlation &#x0028 GCC &#x0029 and steering response power &#x0028 SRP &#x0029 methods based on microphone array are used to estimate the noise source location. Then, the ANC system selects the proper reference microphone for a noise control algorithm. The simulation and experiment results show that the SRP method can estimate the noise source direction with 84&#x0025 accuracy. The proposed microphone array integrated ANC system can dramatically improve the system performance.