Active Noise Control Algorithm Research Articles

Filtered-x generalized mixed norm (FXGMN) algorithm for active noise control

• The filtered-x generalized mixed norm (FXGMN) algorithm for active noise control. • A convex combination of the FXGMN algorithm (C-FXGMN) for active noise control. • The stability condition of the proposed algorithm is analyzed, and computational complexity is provided. • Computer simulations demonstrate robust performance for impulsive noise control. The standard adaptive filtering algorithm with a single error norm exhibits slow convergence rate and poor noise reduction performance under specific environments. To overcome this drawback, a filtered-x generalized mixed norm (FXGMN) algorithm for active noise control (ANC) system is proposed. The FXGMN algorithm is developed by using a convex mixture of l p and l q norms as the cost function that it can be viewed as a generalized version of the most existing adaptive filtering algorithms, and it will reduce to a specific algorithm by choosing certain parameters. Especially, it can be used to solve the ANC under Gaussian and non-Gaussian noise environments (including impulsive noise with symmetric α -stable ( S α S ) distribution). To further enhance the algorithm performance, namely convergence speed and noise reduction performance, a convex combination of the FXGMN algorithm (C-FXGMN) 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 FXGMN and C-FXGMN algorithms can achieve better convergence speed and higher noise reduction as compared to other existing algorithms under various noise input conditions, and the C-FXGMN algorithm outperforms the FXGMN.

An online secondary path modeling method with regularized step size and self-tuning power scheduling.

This paper investigates the active noise control algorithms and improves them by using online secondary path modeling. The proposed method uses three adaptive filters to track the convergence of the system as well as reduce the target noise. By theoretical analysis, the optimized step size and injected random noise gain are derived. The step size is varied according to the convergence of three adaptive filters and the gain of injected random noise is proportional to the power of modeling error, which makes the method more stable even in the presence of strong perturbation. Compared with previous methods, the proposed method improves the convergence rate and estimation accuracy for both the active control system and the secondary path modeling process with less increase of computational complexity. The simulation results verify the above analysis by controlling three different kinds of noise.

Real-time implementation of a robust active control algorithm for narrowband signals suppression

This study presents a practical active noise control (ANC) algorithm with robust stability for reducing the powertrain noise or vibration inside a car. It is important to ensure that a practical ANC system for a car is robustly stable to variations or uncertainties in the actual plant. After investigating the robust stability condition of the ANC algorithm, a robust plant model is designed by considering the multiplicative plant uncertainties within given bounds such as closing or opening door windows. The ANC algorithm was implemented in a dSPACE DS1401 as a control platform, and an error microphone and a subwoofer as a secondary source were positioned at the driver’s left ear and the trunk of the experimental car, respectively. The engine rpm information received from the controller area network of the car was used for the generation of relevant reference signals. The real-time control experiments were carried out against the plant perturbation when the engine was either idling or sweeping in the neutral mode. The results showed that the robust control algorithm can suppress the noise whether the actual plant was nominal or perturbed with the stability over the rpm.

Quiet Zone Enhancement for a Target Location Using an Improved Virtual Sensing Algorithm

Generating a quiet zone at a target location is the ultimate goal of active noise control (ANC). Generally, the best noise cancellation is achieved at the locations of error sensors. However, the distribution of physical error sensors at a given location is not always convenient or feasible. To overcome this challenge, a number of virtual sensing algorithms for ANC have been researched. Using the physical error signals and knowledge of the system, the adaptive least mean square (LMS) virtual sensing algorithm estimates the error signal at a location that is remote from the physical error sensor, referred to as the virtual location. It achieves excellent performance under the assumption that the unknown primary path is fixed, but its performance decreases significantly if the path is constantly changing. This paper presents a real-time ANC system with an improved virtual sensing algorithm to solve this problem. The proposed system was tested to enhance the quiet zone in a constantly changing environment; an enhanced quiet zone was created, and the noise cancellation, especially at the target location, obviously improved.

Efficient algorithm for active noise control of impulsive noise

Active Noise Control (ANC) systems employing adaptive filters suffer from stability issues in the presence of impulsive noise. Due to its simplicity and less computational complexity, the Filtered-x Least Mean Square (FxLMS) algorithm is the most widely used ANC algorithm which minimizes the mean square error of noise, but it lacks robustness and stability in presence of high impulses. To overcome this limitation, new methods must be investigated. A robust adaptive algorithm (Bhagyashri algorithm) for impulsive noise suppression is already proposed in literature. In this paper, Bhagyashri algorithm is tested in ANC domain and thus Filtered-x Bhagyashri (FxBhag) algorithm is proposed. Computer simulations are executed to verify the enhanced performance of the FxBhag algorithm. The statistics of impulsive noise is modeled by Symmetric α-stable (SαS) distributions. The suggested solution exhibits better stability and faster convergence with almost same computational complexity as that of standard FxLMS algorithm and its variants.

A New FXLMS Algorithm With Offline and Online Secondary-Path Modeling Scheme for Active Noise Control of Power Transformers

In this study, the active noise control (ANC) method was used to suppress high-decibel and low-frequency power transformer noise. An appropriate ANC system was selected based on the transformer noise characteristics and experimental condition. A new filter-X least mean square (FXLMS) adaptive ANC algorithm based on offline and online secondary-path modeling was proposed to realize faster and more stable secondary-path online modeling than that of the random white-noise FXLMS algorithm and to ensure the convergence, stability, and reduction in transformer noise control. Moreover, the genetic algorithm is adopted to optimize the convergence coefficient, while the effect of the convergence coefficient on the algorithm was analyzed using simulation and theory. In addition, the transformer noise online monitoring and active control system was designed including software and hardware, and the hardware devices were selected based on the noise feature. In the 50 000 KVA transformer noise reduction experiment, the system achieved a noise reduction of 8–15 dB and an 84.10–96.86% decrease in average sound energy density in a certain area.

Active control of wind turbine noise using audio integrated pillows

This paper presents integration of active noise control (ANC) system with auditory masking technique to cancel wind turbine noises on pillows and improve sleep quality. By using recorded wind turbine noise, the broadband feedforward and the adaptive feedback ANC algorithms are developed to conduct experiments in real-time. Experimental results show that the feedforward ANC systems reach better performance than the feedback ANC system, and the auditory masking technique can be effectively integrated with the ANC algorithms to mask the residual noise. The generated quiet zone can cover the pillow to support free head movement.

A novel cost-effective parallel narrowband ANC system with local secondary-path estimation

Many noise reduction applications are targeted at multi-tonal disturbances. Active noise control (ANC) solutions for such problems are generally based on the combination of multiple adaptive notch filters. Both the performance and the computational cost are negatively affected by an increase in the number of controlled frequencies. In this work we study a different modeling approach for the secondary path, based on the estimation of various small local models in adjacent frequency subbands, that greatly reduces the impact of reference-filtering operations in the ANC algorithm. Furthermore, in combination with a frequency-specific step size tuning method it provides a balanced attenuation performance over the whole controlled frequency range (and particularly in the high end of the range). Finally, the use of small local models is greatly beneficial for the reactivity of the online secondary path modeling algorithm when the characteristics of the acoustic channels are time-varying. Several simulations are provided to illustrate the positive features of the proposed method compared to other well-known techniques.

NLFXLMS and THF‐NLFXLMS Algorithms for Wiener‐Hammerstein Nonlinear Active Noise Control

AbstractNonlinear Filtered‐X LMS (NLFXLMS) is an indirect adaptive control algorithm for nonlinear active noise control (NANC) system. The algorithm has been developed for both Hammerstein and Wiener secondary paths where the nonlinearity is represented by scaled error function (SEF) and tangential hyperbolic function (THF). NLFXLMS algorithm is limited in practical application because the degree of nonlinearity has to be known in advance. This limitation leads to the development of the THF‐NLFXLMS algorithm where the degree of nonlinearity is estimated by modelling the secondary path. In this work, the NLFXLMS and THF‐NLFXLMS are extended to Wiener‐Hammerstein system. The performance of the proposed Wiener‐Hammerstein THF‐NLFXLMS is compared with NLFXLMS algorithm which is considered as the benchmark and second order Volterra algorithm of comparable computational complexity. Simulation results show that the THF‐NLFXLMS has a similar performance to NLFXLMS and outperforms the second order Volterra algorithm as the system becomes more nonlinear.

Performance validation of printed strain sensors for active control of intelligent tires

To ensure safety and comfort, the automotive industry employs sensors to monitor the vehicle dynamics, with growing interest in intelligent tires. This paper specifically presents the design and validation of a dynamic load sensor proposed for integration in tires to allow active control of noise inside the cabin of the car. The concept of using tire surface strain measurement is first experimentally investigated using a quarter-car test bench, through coherence evaluation between the vertical force injected at the wheel hub and the out-of-plane velocity field over the lateral surface of the tire, as measured using a scanning laser Doppler vibrometer (LDV). The tire sidewall deformations are found to correlate well with vertical road excitations, suggesting that strain measurement on the tires could a useful signal for noise control. Specifications are then established for a sensor to measure strain on the tire, leading to the design and fabrication of a Capacitive Strain Transducer (CST) using printed electrodes on a flexible substrate. A number of CSTs are bonded on the tire and characterized to determine optimal sensor locations. The coherence achieved between the CST measurements and the excitation force is compared with a reference Macro Fiber Composite (MFC) transducer. The CST shows promise for providing the reference signal required in feedforward algorithms for active noise and vibration control inside the car, since it shows good coherence with the force reference signal, but only for relatively large loads.

Adaptive nonlinear active noise control algorithm for active headrest with moving error microphones

Nonlinear active noise control (ANC) algorithms are used to control nonlinear noise processes. In this paper, a generalized filter bank based nonlinear ANC algorithm is proposed for active headrest application. The algorithm is used to control the noise at both ears by using two moving error microphones which are mounted on the head. Mounting of microphones can be made using a wearing item such as a hat or band. Due to this, these microphones move along with the head. The adaptive ANC algorithm is shown to be very effective for head-movement. Real-time implementation of nonlinear ANC algorithm is made using a dSpace 1104 system for both ears. The robust performance of adaptive nonlinear ANC algorithm is shown for controlling nonlinear noise process at both ears even during head movement using a pair of moving error microphones without re-estimating the secondary paths for the new position.

Adaptive Active Noise Suppression Using Multiple Model Switching Strategy

Active noise suppression for applications where the system response varies with time is a difficult problem. The computation burden for the existing control algorithms with online identification is heavy and easy to cause control system instability. A new active noise control algorithm is proposed in this paper by employing multiple model switching strategy for secondary path varying. The computation is significantly reduced. Firstly, a noise control system modeling method is proposed for duct-like applications. Then a multiple model adaptive control algorithm is proposed with a new multiple model switching strategy based on filter-u least mean square (FULMS) algorithm. Finally, the proposed algorithm was implemented on Texas Instruments digital signal processor (DSP) TMS320F28335 and real time experiments were done to test the proposed algorithm and FULMS algorithm with online identification. Experimental verification tests show that the proposed algorithm is effective with good noise suppression performance.

Simulation Study of Active Noise Control in Wind Turbines Using FxLMS Adaptation Algorithm

Utility scale wind turbines produce a significant amount of noise which has been identified as one of the most critical challenges to the widespread use of wind energy. Aerodynamic noise caused primarily by the interaction of the boundary layer and (or) the upstream atmospheric turbulence with the trailing edge of the blade has been identified as the most dominant source of noise in wind turbines. The authors here propose an active noise control system based on the FxLMS algorithm which can achieve suppression of noise from a modern wind turbine. Two types of noise sources have been simulated: monopole and dipole. The results of the active noise control algorithm are validated with simulations in MATLAB. The agreement between the results shows the far impact of active noise control techniques will have in future wind turbines.

Frequency-domain active noise control for magnetic resonance imaging acoustic noise

The purpose of this paper is to propose a frequency-domain active noise control algorithm for acoustic noise reduction in magnetic resonance imaging and experimentally verify its feasibility. The sound pressure level of noise approaches up to 130dB in 3T magnetic resonance imaging. Even it’s expected to increase for higher-resolution images in the future magnetic resonance. For this noise, some active methods have been researched, but the reduction performance is not enough to satisfy the safety standard. The noise in 3T magnetic resonance imaging is measured and analyzed in the time-frequency domain. It has fundamental frequency with its higher harmonics, and the Fourier coefficients of all peaks are slowly varying over time. The variation is small enough, so it can be assumed that the noise is periodic in short time. Based on this assumption, the control signal is determined as the sum of sinusoidal signals designed by using the Fourier coefficients calculated from a fundamental period of noise, then it is applied to the next period. To do this in real-time process, domain transform algorithm, which is time to frequency based on discrete Fourier series, is developed with the same level of computational complexity compared to the conventional active noise control algorithm. Experimental results show approximately 35-dB overall reduction in the frequency range of interest (80–1600Hz).

Improving performance of FxRLS algorithm for active noise control of impulsive noise

Active noise control (ANC) systems employing adaptive filters suffer from stability issues in the presence of impulsive noise. New impulsive noise control algorithms based on filtered-x recursive least square (FxRLS) algorithm are presented. The FxRLS algorithm gives better convergence than the filtered-x least mean square (FxLMS) algorithm and its variants but lacks robustness in the presence of high impulsive noise. In order to improve the robustness of FxRLS algorithm for ANC of impulsive noise, two modifications are suggested. First proposed modification clips the reference and error signals while, the second modification incorporates energy of the error signal in the gain of FxRLS (MGFxRLS) algorithm. The results demonstrate improved stability and robustness of proposed modifications in the FxRLS algorithm. However, another limitation associated with the FxRLS algorithm is its computationally complex nature. In order to reduce the computational load, a hybrid algorithm based on proposed MGFxRLS and normalized step size FxLMS (NSS-FXLMS) is also developed in this paper. The proposed hybrid algorithm combines the stability of NSS-FxLMS algorithm with the fast convergence speed of the proposed MGFxRLS algorithm. The results of the proposed hybrid algorithm prove that its convergence speed is faster than that of NSS-FxLMS algorithm with computational complexity lesser than that of FxRLS algorithm.

The MMFxLMS algorithm for active noise control with on-line secondary path modelling

This paper presents an architecture namely the Mirror MFx (MMFx) for adapting adaptive filtering algorithms for Active Noise Control (ANC) with on-line secondary path modelling. The proposed architecture is used in conjunction with the LMS algorithm, resulting in the MMFxLMS algorithm. A time domain analysis of the algorithm is presented, showing that the algorithm converges regardless of secondary path modeling errors. Simulations of the algorithm in different conditions but with the same parameters result in 100% convergence. The algorithm is especially suited to deal with large and sudden changes in the secondary path when the ANC system is in operation. Comparisons with competing algorithms are made, showing that they do not reach the same performance.

Improved Filtered-x Least Mean Kurtosis Algorithm for Active Noise Control

The least mean kurtosis (LMK) algorithm has been successfully applied to linear system identification. It outperforms the conventional least mean square method for Gaussian and non-Gaussian noises....

능동 소음 제어를 위한 정규화된 다채널 FxLMS 알고리즘

본 논문에서는 다채널 능동 소음 제어를 위한 적응 필터에 적용할 수 있는 정규화된 FxLMS(Filtered-x Least Mean Square) 알고리즘을 제안하였다. 단일 채널 능동 소음 제어를 위한 FxLMS 알고리즘의 경우는 기존의 NLMS(Normalized Least Mean Square) 알고리즘과 같은 방식으로 정규화할 수 있는 반면, 다채널 능동 소음 제어의 경우에는 단일 채널 방식의 정규화 알고리즘을 그대로 적용할 수 없다. 먼저, 최소 교란 원리에 근거한 일반화된 정규화 알고리즘을 이용하여, 역행렬 연산을 피하기 위하여 대각 성분만을 고려한 정규화 알고리즘을 제안하였다. 컴퓨터 모의 실험을 통하여 제안된 알고리즘을 정규화되지 않은 기존의 알고리즘들과 비교하였다. 제안된 알고리즘이 정규화되지 않은 기존의 알고리즘에 비하여 비정상 환경에서 우수한 성능을 가진다는 것을 보였다. In this paper, we propose a normalization algorithm that can be applied to adaptive filters for multi-channel active noise control. The FxLMS (Filtered-x Least Mean Square) algorithm for the single-channel active noise control can be normalized in the same way as the NLMS (Normalized Least Mean Square) algorithm, whereas in case of the multi-channel active noise control, the single-channel normalization for the FxLMS algorithm cannot be extended to the normalization for the multi-channel FxLMS algorithm straightforwardly. First, we adopt a generalized normalization algorithm for the multi-channel FxLMS algorithm based on the principle of minimal disturbance and then, proposed a normalized algorithm considering only diagonal elements to avoid computation for matrix inversion. We carried out performance comparisons of the proposed algorithm with other algorithms without normalization. It is shown that the proposed algorithm presents better convergence characteristics under non-stationary environments.

A Simplified Subband ANC Algorithm Without Secondary Path Modeling

Active noise control algorithms without secondary path modeling are very appealing because they can effectively overcome the problems caused by imperfect secondary path model. For broadband noise control, subband structures have been adopted in these kinds of algorithms, where the full-band adaptive filter coefficients are obtained from subband filter coefficients with weight transformation. However, the usually used two-direction search method suffers from slow convergence if the secondary path phase is close to $\pm 90^\circ $ . To deal with this problem, a four-direction search method has been proposed in frequency domain, which is sometimes too complicated for practical applications. In this paper, a simplified algorithm is proposed to reduce the implementation complexity. In the proposed algorithm, the delay of the secondary path is estimated first, and then the estimated delay is used in the delay compensation to reduce the number of subbands. Two reference signals are generated in each subband and two update directions are used. Only one subband reference signal and one update direction are selected to approximate the phase response of the residual secondary path, and then the full-band adaptive controller coefficients are adjusted directly in time domain with them without the need of further weight transformation. Just like those existing subband algorithms, the simplified algorithm can obtain similar steady-state noise reduction performance as that of the FxLMS algorithm but without secondary path modeling. Simulations and experiments are carried out to demonstrate the feasibility of the proposed algorithm.

Robust adaptive algorithm for active control of impulsive noise

Active noise control (ANC) systems employing adaptive filters suffer from stability issues in the presence of impulsive noise. To overcome this limitation, new methods must be investigated. In this paper, we propose the filtered-x state-space recursive least square (FxSSRLS), an SSRLS-based practical and adaptive algorithm for ANC. Computer simulations are executed to verify the enhanced performance of the FxSSRLS algorithm. Symmetric α-stable (SαS) distributions are used to model impulsive noise. The results show that the proposed FxSSRLS algorithm is more robust in eliminating high-peaked impulses than the recently reported algorithms for ANC applications. Moreover, the suggested solution exhibits better stability and faster convergence, without jeopardizing the performance of the proposed solution in terms of residual noise suppression in the presence of impulses.