Active Noise Control Algorithm Research Articles

M-max partial update leaky bilinear filter-error least mean square algorithm for nonlinear active noise control

To reduce the computational burden of the bilinear FLANN (BFLANN) filter for active noise control (ANC), an M-max partial update leaky bilinear filtered-error least mean square (MmLBFE-LMS) algorithm is proposed in this paper. Unlike the algorithm based on filtered-reference technique in BFLANN-based ANC system, the proposed MmLBFE-LMS algorithm uses the filtered-error method and data-dependence partial update strategy to reduce computational complexity, and employs a leaky technique to mitigate the instability problem as in bilinear filters. The simulation results and computational complexity analysis indicate that the proposed algorithm can significantly reduce the computational burden of the BFLANN-based ANC system without suffering from noise-canceling performance degradation.

Application Study of Adaptive Tracking Algorithm in Active Noise Control System of Transformer

Active noise control (ANC) technology can be able to reduce the low frequency noise effectively, and has been widely applied in limited enclosed equipment, such as cars, aircraft cockpits, and headphones, etc. Compared with the traditional noise reduction methods, ANC technology is a cost-effective method in handling the low-frequency noise of transformers. An experimental ANC system in a virtual substation is designed and constructed. An adaptive signal tracking ANC algorithm is proposed, and a set of ANC experimental prototypes applied to a virtual substation is designed. Based on the proposed algorithm, the distributions of the acoustics and spectral characteristics of transformer noise in substations are analyzed. Experimental results show that the effective noise reduction can be achieved an average of 5 dB(A) in a given region with respect to the absence of the ANC system.

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.

CPU-GPU architecture for active noise control

Active noise control (ANC) is a technology which lowers the noise level by using the principle of destructive interference of sound wave. Performance of active noise control, generally, is highly related to the number of reference transducers, secondary speakers, and error microphones. However, the more number of transducers, microphones and speakers are used for improving ANC performance, the higher computational requirements of such an ANC algorithm would be. Currently, limited computational efficiency of conventional DSP systems hinders the possible maximum noise reduction performance of ANC. In the previous research, for figuring out the limited ANC performance issues due to a lack of computational efficiency, graphics processing units (GPU), which possesses significant parallel computational efficiency, is utilized for ANC algorithms. However, ANC algorithms with GPU computations in the previous researches have limitations on the fact that they are designed in frequency domain structure due to a block data transfer between central processing units (CPU) and GPU, which suffers from a block delay in control signals. Therefore, throughout this research, we proposed a CPU-GPU architecture for ANC algorithms, which does not suffer from the problems of frequency domain algorithms and take advantage of GPU computation. Also, we suggested important factors needed to be concerned when an ANC algorithm based on CPU-GPU architecture is being developed. The feasibility of the proposed CPU-GPU architecture for ANC algorithms is shown with an example of implementing the proposed CPU-GPU architecture on multichannel delayless subband ANC algorithm.

Global active control of harmonic noise in a vibro-acoustic cavity using Modal FxLMS algorithm

The Modal FxLMS algorithm is recently proposed for reduction of global level of noise in vibro-acoustic cavities. The algorithm minimizes acoustic potential energy expressed in modal domain which allows a choice of acoustic modes whose modal amplitudes are desired to be minimized. The working of the algorithm and its effectiveness has been demonstrated previously through a numerical study and an experimental study is required to test the effectiveness of the algorithm in practice. To meet this objective this paper presents an experimental study of this algorithm for global active noise control in a rectangular box cavity. The algorithm utilizes modal filtered reference signals and modal amplitudes of the selected acoustic modes of the cavity. To identify modal filtered reference signals, modal secondary paths are utilized. In the present study, modal secondary paths are identified using experimentally identified physical secondary paths and acoustic mode shapes. Modal amplitudes of the acoustic modes are identified on the basis of acoustic pressure measurements from eight microphones suitably placed throughout the cavity. The performance of the algorithm is studied under the presence of acoustic and structural disturbances using one or two control loudspeakers. The active control is carried out at harmonic frequencies coinciding with cavity-controlled resonances and panel-controlled resonances. It is found from the experimental results that the Modal FxLMS algorithm is successful in reducing modal amplitudes of the selected acoustic modes. When all the significantly contributing acoustic modes are chosen, a reduction in global level of noise is observed at cavity as well as panel controlled resonances.

An efficient algorithm for nonlinear active noise control of impulsive noise

Nonlinear active noise control (NANC) systems employing Volterra filter suffer from the stability issues in the presence of impulsive noise. To solve this problem, we combine the second-order Volterra (SOV) filter and maximum correntropy criterion (MCC) in this paper. The Volterra filter-x maximum correntropy criterion (VF × MCC) algorithm and Volterra filter-x recursive maximum correntropy (VF × RMC) algorithm are applied to reduce the impulsive noise of NANC. We find that VF × MCC algorithm has a low computational complexity and VF × RMC algorithm converges fast. In order to extract their advantages, we further propose a hybrid algorithm based on the VF × MCC and VF × RMC algorithms. In addition, the normalize step-size version of VF × MCC (VF × nMCC) algorithm is developed to improve the robustness and performance. Meanwhile, we adaptively adjust the kernel size of MCC online based on the sample variance of reference signal to improve the performance of the proposed algorithms. Simulation results in the context of nonlinear active impulsive noise control demonstrate that the proposed algorithms achieve much better performance than the existing algorithms in various noise environments.

Spatial Noise-Field Control With Online Secondary Path Modeling: A Wave-Domain Approach

Due to strong interchannel interference in multichannel active noise control (ANC), there are fundamental problems associated with the filter adaptation and online secondary path modeling remains a major challenge. This paper proposes a wave-domain adaptation algorithm for multichannel ANC with online secondary path modelling to cancel tonal noise over an extended region of two-dimensional plane in a reverberant room. The design is based on exploiting the diagonal-dominance property of the secondary path in the wave domain. The proposed wave-domain secondary path model is applicable to both concentric and nonconcentric circular loudspeakers and microphone array placement, and is also robust against array positioning errors. Normalized least mean squares-type algorithms are adopted for adaptive feedback control. Computational complexity is analyzed and compared with the conventional time-domain and frequency-domain multichannel ANCs. Through simulation-based verification in comparison with existing methods, the proposed algorithm demonstrates more efficient adaptation with low-level auxiliary noise.

Frequency-Domain Filtered-x LMS Algorithms for Active Noise Control: A Review and New Insights

This paper presents a comprehensive overview of the frequency-domain filtered-x least mean-square (FxLMS) algorithms for active noise control (ANC). The direct use of frequency-domain adaptive filters for ANC results in two kinds of delays, i.e., delay in the signal path and delay in the weight adaptation. The effects of the two kinds of delays on the convergence behavior and stability of the adaptive algorithms are analyzed in this paper. The first delay can violate the so-called causality constraint, which is a major concern for broadband ANC, and the second delay can reduce the upper bound of the step size. The modified filter-x scheme has been employed to remove the delay in the weight adaptation, and several delayless filtering approaches have been presented to remove the delay in the signal path. However, state-of-the-art frequency-domain FxLMS algorithms only remove one kind of delay, and some of these algorithms have a very high peak complexity and hence are impractical for real-time systems. This paper thus proposes a new delayless frequency-domain ANC algorithm that completely removes the two kinds of delays and has a low complexity. The performance advantages and limitations of each algorithm are discussed based on an extensive evaluation, and the complexities are evaluated in terms of both the peak and average complexities.

Integrated psychoacoustic active noise control and masking

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.

A normalized frequency-domain block filtered-x LMS algorithm for active vehicle interior noise control

Abstract A novel normalized frequency-domain block FxLMS (NFB-FxLMS) algorithm is proposed for active vehicle interior noise control. Different from the time-domain FxLMS (TD-FxLMS) algorithm that processes noise data point by point, the NFB-FxLMS algorithm processes noise data block by block based on the fast Fourier transform (FFT) and the overlap–save method, such that the calculation amount is considerably reduced. In the process, the input signal of a control filter and the filtered input signal are transferred to frequency domain, respectively. Meantime, its convergence performance is improved by normalizing each frequency-domain bin. The interior noise of a high-speed vehicle is recorded and analyzed. The simulations of ANC based on the TD-FxLMS, normalized TD-FxLMS (TD-NFxLMS), basic FB-FxLMS, and NFB-FxLMS algorithms are performed. The results show that the interior noises are suppressed by these four algorithms. But the NFB-FxLMS with greater stability has faster convergence rate and lower steady-state error in the entire frequency band compared with other three algorithms. The higher the vehicle speed, the more obvious the advantages of the NFB-FxLMS. Furthermore, the NFB-FxLMS shows very good effect on nonstationary noise. The NFB-FxLMS algorithm can be directly used in an active interior noise control system of the sample vehicle, which can be extended to other types of vehicle for interior noise control.

Filtered-x least mean square/fourth (FXLMS/F) algorithm for active noise control

The filtered-x least mean square (FXLMS) algorithm is widely used for active noise control (ANC) systems. However, due to the fixed step-size of FXLMS algorithm being used, the FXLMS algorithm results in a compromise between noise reduction performance and convergence speed. Therefore, this paper proposes the filtered-x least mean square/fourth (FXLMS/F) algorithm for ANC systems, which can be viewed as a variable step-size FXLMS (VSS-FXLMS) algorithm. In order to further improve the algorithm performance, the convex combination of the FXLMS/F (C-FXLMS/F) algorithm for ANC systems is presented. Moreover, the computational complexity of the proposed algorithms is analyzed, and a stability condition for the proposed algorithms is provided. Simulation results show that the proposed FXLMS/F and C-FXLMS/F algorithms can achieve better convergence performance as compared to the FXLMS and FXLMF algorithms under various noise input conditions, and the C-FXLMS/F algorithm outperforms the FXLMS/F algorithm.

All-pass filtered x least mean square algorithm for narrowband active noise control

Active noise control (ANC) is the most popular method for attenuating acoustic primary noise or disturbances by generating controllable secondary sources by which the output noise can be cancelled with the same amplitude but with opposite sign/sense. Most available ANC uses the secondary path modelling including filtered x least mean square (FxLMS) algorithm. The modelling requirement of the secondary path increases the complexity of the system implementation and decreases the control system performance. Recently several new ANC algorithms have been developed; in which there is no requirement for the modelling of the secondary path transfer function. In this regard, the aim of this paper is concerned about a narrowband feedforward ANC system in systems like air intake duct system and its novelty is to introduce all-pass filtered x LMS (APFxLMS) algorithm which do not require an estimation of the secondary path. Here first-order all pass filters with single parameter is used to improve the convergence of the LMS algorithm. The performance evaluation in terms of convergence speed of the proposed algorithm is validated with standard ANC without secondary path modelling. The results also show that the proposed method outperforms other LMS algorithm without secondary path modelling. The proposed narrowband LMS algorithm would benefit in the design of efficient feedforward ANC system that can realize noise control in air intake duct applications.

Functional link artificial neural network filter based on the q-gradient for nonlinear active noise control

As one of the most commonly used nonlinear active noise control (NANC) algorithms, the filtered-s least mean square (FsLMS) algorithm outperforms the conventional filtered-x least mean square (FxLMS) algorithm when the primary path has a quadratic nonlinearity. However, it still suffers from performance degradation under strong interferences. In this paper, two new algorithms, named filtered-s q-least mean p-norm (FsqLMP) and filtered-s q-least mean square (FsqLMS), based on the concept of Jackson's derivative, are proposed. By using new Jackson's derivative method, the proposed algorithms are less sensitive to the interferences in NANC system. Additionally, it is shown that the family of q-least mean square algorithms are special cases of the proposed FsqLMP algorithm. To further improve performance of the FsqLMS algorithm and solve the parameter selection problem, a time varying q scheme is developed. Simulation studies indicate that the proposed algorithms provide superior performance in various noise environments as compared to the existing algorithms.

Two-gradient direction FXLMS: An adaptive active noise control algorithm with output constraint

Abstract Active noise control (ANC) is broadly used to cancel the unwanted disturbance in different fields because of its excellent performance in abating low-frequency noise. In practice, however, the limited driving capability of actuators restrict the maximum output power of ANC systems. Once the driving signal of the ANC system exceeds these limitations, the inherent nonlinearity of the actuators will deteriorate the noise reduction and may result in the divergence of the adaptive algorithm. Hence, the two-gradient direction filtered-x least mean square (2GD-FXLMS) algorithm based on the optimal Kuhn-Tucker solution with the output constraint is proposed in this paper. This algorithm has the advantage of minimizing system overdriving, maintaining a specified power budget, and enhancing system stability. Compared to existing output-constrained adaptive algorithms, this proposed algorithm has the same computational complexity as the conventional FXLMS algorithm, while maintaining a stricter output constraint that minimizes the saturation distortion.

PSO based adaptive narrowband ANC algorithm without the use of synchronization signal and secondary path estimate

Abstract Narrowband active noise control (ANC) conventionally uses the waveform synthesis method in which a synchronization signal is essential. In addition to this, to update the parameters of the controller, prior estimation of the secondary path is required. Any mismatch in the frequency of the synchronization signal and the secondary path estimate compared to actual leads to a degraded performance in noise control. In this paper, a new type of narrowband ANC algorithm is proposed which requires neither the synchronization signal nor the secondary path estimate for controller tuning. Therefore, the algorithm is free from the effect of the change in the source frequency and the secondary path transfer function. The proposed adaptive algorithm is based on a non-gradient type optimization algorithm named as particle swarm optimization (PSO) algorithm. Through exhaustive computer based simulation, it has been demonstrated that the algorithm is not only capable to control a monotone noise but also efficiently control a multi-tone noise. The algorithm, due to its adaptive nature, is also capable of tracking the change in amplitude and frequency of the noise as well as, the change in the characteristics of primary and secondary paths.

Kernel Filtered-x LMS Algorithm for Active Noise Control System with Nonlinear Primary Path

In active noise control (ANC) systems, the primary path may exhibit nonlinear impulse responses. Conventional linear ANC controllers based on a filtered-x least mean square (FxLMS) algorithm exhibit performance degradation when compensating for nonlinear distortions of the primary path. Several nonlinear active noise control algorithms, including Volterra filtered-x least mean square (VFxLMS) and filtered-s least mean square (FsLMS), have been utilized to overcome this nonlinear effect. However, the performance still needs to be improved when the reference noise is mixed with multiple narrowband signals and additional Gaussian white noise. Over the last several years, kernel adaptive filters have exhibited powerful capabilities in multiple signal processing domains. When kernel adaptive filters are introduced into the ANC system, a great challenge is to compensate for the inherent delay caused by the secondary path. Due to the implicit mapping of the kernel method, it is difficult to filter the reference signal in the high-dimensional feature space. In this paper, an approximate method is proposed in which the filtered reference signal is mapped to the high-dimensional feature space. In addition, a kernel filtered-x least mean square (KFxLMS) algorithm is developed for an ANC system with a nonlinear primary path. Simulation experiments demonstrate that the performance of the proposed KFxLMS algorithm is better than that of the FxLMS, VFxLMS, and FsLMS algorithms.

Active Noise Control Over Space: A Wave Domain Approach

Noise control and cancellation over a spatial region is a fundamental problem in acoustic signal processing. In this paper, we utilize wave-domain adaptive algorithms to iteratively calculate the secondary source driving signals and to cancel the primary noise field over the control region. We propose wave-domain active noise control algorithms based on two minimization problems: first, minimizing the wave-domain residual signal coefficients, and second, minimizing the acoustic potential energy over the region, and derive the update equations with respect to two variables, the loudspeaker weights and wave-domain secondary source coefficients. Simulation results demonstrate the effectiveness of the proposed algorithms, more specifically the convergence speed and the noise cancellation performance in terms of the noise reduction level and acoustic potential energy reduction level over the entire spatial region.

Modal filtered-x LMS algorithm for global active noise control in a vibro-acoustic cavity

Abstract This paper proposes an algorithm called as 'Modal filtered-x LMS algorithm' for global active noise control in vibro-acoustic cavities. The proposed algorithm is a formulation of the conventional filtered-x least mean square algorithm in modal domain to reduce acoustic potential energy in a cavity. The formulation leads to a concept of what has been called as 'modal secondary path' in the paper. The active control method makes use of the reference signal filtered using modal secondary paths instead of the reference signal filtered using physical secondary paths as done in the conventional FxLMS algorithm. The method requires knowledge of acoustic modal pressures which in the present paper are estimated using acoustic pressure measurements at a discrete number of points in the cavity. The proposed method allows a modal based control of acoustic potential energy in the cavity. The proposed method also has an advantage that it reduces the computational burden associated with filtering of the reference signal with secondary paths for global control. A numerical study to actively control noise due to structural and acoustic disturbances acting on a car-like cavity is presented. Results of modal based control are compared with maximum reduction that is possible for given disturbances and control sources. It is found that the noise reduction obtained using the modal based approach is close to the maximum possible but at a lower computational cost.

An interactive simulation of the filtered-x least-mean-squares algorithm for active noise control

As part of a graduate acoustics course at Brigham Young University, students learn about principles of active noise control (ANC). One of the concepts covered is how a basic ANC algorithm functions. This presentation describes an interactive simulation of the filtered-x least-mean-squares (FXLMS) algorithm implemented in MATLAB. Available user-selected inputs include sampling frequency, type of signal to be controlled, number of filter coefficients, convergence coefficient, and system model parameters. As outputs, the students are able to observe the different signals, the attenuation, and the filter coefficients. Altering inputs and observing the simulation results motivates classroom discussion and facilitates students' gaining a working understanding of an ANC algorithm.