Feedback Active Noise Control Research Articles

A Feedback Active Control Approach to Road Noise Based on a Single Microphone Sensor to Improve Automotive Cabin Sound Comfort.

Tire-road noise deteriorates the sound quality of a vehicle's interior and affects the driving safety and comfort. Obtaining low interior noise is a challenge for passenger car manufacturers. Traditional passive noise control (PNC) is efficient for canceling high frequency noise but not useful for low frequency noise, while active noise control (ANC), according to the residual error signal, can generate an anti-noise signal to reduce the original noise. Most research has focused on improving the control effect for a feedforward ANC system. However, this paper emphasizes a feedback ANC system based on a signal microphone sensor. There are two main contributions in this study to improve automotive cabin sound comfort. One is that the algorithm of the feedback ANC system using a single microphone sensor without a reference noise signal is proposed based on the Filtered-x Least Mean Square method. The other is that the algorithm applies additive random noise online to estimate the secondary path model. A simulation was implemented based on measured real road noise data, and the simulation results indicate that the proposed feedback ANC system with the single microphone sensor can effectively attenuate road noise. This study shows the feasibility of applying a feedback ANC system in automobiles to increase the cabin sound quality.

A Simplified Frequency-Domain Feedback Active Noise Control Algorithm

When the adaptive filter length is increased, the calculation complexity increases rapidly because the relationship between the calculation and the adaptive filter length N contains a power function with no secondary path identification algorithm. Under the basic premise of unreduced noise reduction, herein, a simplified frequency-domain feedback active noise control algorithm is proposed. To reduce the computation complexity, the total delay is adopted as the estimated secondary path; the filtered reference signal is produced in the frequency domain by using multiplication to replace convolution calculation in the time domain and then updating the adaptive filter coefficients in the frequency domain. Therefore, the computational complexity has a logarithmic function with the increased adaptive filter length in the proposed algorithm. If the adaptive filter length is 512, the existing WSMANC algorithm’s calculation is 271,360 real number multiplications, while that of the proposed algorithm is only 38,912 real number multiplications. To verify the proposed algorithm’s stability, convergence speed, and noise reduction, the single-frequency noise, narrowband white noise, and narrowband pink noise, respectively, are used as the primary noise types in the simulations. The results show that (1) the proposed SFDFBANC algorithm can obtain similar noise reduction performance to existing algorithm, (2) the convergence rate is faster than existing algorithm, and (3) if the adaptive filter length is more than 64, the proposed algorithm exhibits a lower computational complexity.

Research on the Active Noise Control Technology for the Reduction of the Air Conditioner Noise in a Vehicle Cabin

In this study, an attempt to reduce the flow-induced noise generated by heating, ventilation, and air-conditioning (HVAC) systems for commercial electric vehicles using feedback active noise control with real-time filter update in frequency domain was conducted to improve the quietness of a running vehicle. In active noise control (ANC), the frequency-domain gradient descent algorithms (FDGD) to adapt the noises was studied. In the experiment, air conditioner noise was measured using a microphone placed at the driver's left ear and referenced by a microphone at a map lamp for ANC. Results shows that in the 1/3-octave band, the sound pressure level in driver's left ear was 7.2 dB smaller at maximum when the control was operated. The effective frequency bands were between 50 and 150 Hz. Its reduction was 4.7 dB on average.

An analysis of a feedback active noise control system using the remote microphone technique

This paper examines strategies in virtual sensing feedback active noise control systems and compares the noise reduction performance of three strategies: using physical microphones for direct feedback control, using virtual microphones for direct feedback control, and using physical microphones for indirect virtual feedback control. For a primary sound field with incident waves in a single direction, simulation results indicate that employing a physical microphone for indirect virtual feedback control consistently yields superior noise reduction performance than that using a physical microphone for direct feedback control. However, if the causal requirement between physical and virtual microphones is not satisfied, the increase in the equivalent secondary path delay deteriorates noise reduction performance while utilizing physical microphones for indirect virtual feedback control. For a primary sound field containing waves from multiple directions, the simulation results show that the noise reduction performance is affected by both the coherence and causality between the physical and virtual microphone signals.

Careful feedback active noise and vibration control algorithm robust to large secondary path changes

Feedback active noise and vibration control (ANVC) reduces narrowband noise and vibration when there is no reference signal. However, most ANVC algorithms require a secondary path (plant) model and can become unstable when this model is inaccurate. This work proposes an algorithm less sensitive to plant modeling errors and can cope with large and fast plant changes. This is achieved by modeling the plant and the noise using an autoregressive exogenous input (ARX) model. This model allows obtaining the expected values of the future residual noise and determining the optimal control signal. Since the model coefficient uncertainty (variance) is considered when calculating the expected value, the resulting controller is a careful controller. This allows solving problems due to significant model estimation errors when information is insufficient to estimate the full ARX model.

The Predict and Invert Feedback Active Noise and Vibration Control Algorithm

This work proposes an algorithm for feedback ANC that does not require a prior secondary path model and usually remains stable after fast secondary path changes, as other algorithms proposed for feedforward ANC. This is achieved using a recursive least squares algorithm to model the secondary path and the primary noise with an autoregressive moving average model. The resulting model allows for predicting future values of the primary noise. Finally, the primary noise values predicted are filtered by a non-causal inverse of the secondary path model to generate the anti-noise signal. Simulation results attest to the validity of the algorithm in reducing narrowband noise.

Stability Guaranteed Active Noise Control: Algorithms and Applications

Stability of the controller, robust stability of the active noise control (ANC) system, and limited output power of the hardware control board or actuators are crucial issues for ANC applications. Violation of these requirements results in output saturation, and hence nonlinear distortion by overloading the actuators. As a consequence, noise reduction performance of an ANC system will be dramatically degraded. A novel frequency-weighted leaky filtered-x least-mean-square (FxLMS) algorithm is developed in this article to fulfill these requirements. The proposed algorithm is an improvement of the traditional leaky FxLMS algorithm, where a frequency-weighted matrix is designed to replace the corresponding scalar weight. The frequency-weighted matrix, which is achieved by solving a constrained optimization problem, consists of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\it N$</tex-math> </inline-formula> frequency-weighted factors. By the frequency-weighted factors, the frequency response of the control filter is only suppressed at specific frequencies where the requirements on stability and/or limited output power are violated. Thus, the proposed control algorithm achieves better noise reduction performance than the traditional leaky FxLMS algorithm, which unnecessarily suppresses the control filter at all the frequencies. Simulations and experiments are carried out in a duct ANC system to verify the effectiveness of the proposed algorithm for both the feedforward and feedback ANC systems.

DNoiseNet: Deep learning-based feedback active noise control in various noisy environments

The use of active noise control/cancelation (ANC) has increased because of the availability of efficient circuits and computational power. However, most ANC systems are based on traditional linear filters with limited efficiency due to the highly nonlinear and nonstationary nature of various noises. This paper proposes an advanced deep learning–based feedback ANC named DNoiseNet that overcomes the limitations of traditional ANCs and addresses primary and secondary path effects, including acoustic delay. Mathematical operators (i.e., atrous convolution, pointwise convolution, nonlinear activation filters, and recurrent neural networks) learn multilevel temporal features under different noises in various environments, such as construction sites, vehicle interiors, and airplane cockpits. Due to the nature of feedback control using a single error sensor, an estimation of the reference noise signal must be regenerated. In this paper, a multilayer perceptron (MLP) neural network–based secondary path estimator is also proposed to improve the performance of DNoiseNet. In extensive parametric and comparative studies, the DNoiseNet with the MLP secondary path estimator exhibited the best performance in root mean square error and noise attenuation metrics.

Implementations of wireless active noise control in the headrest

Working from home went from a temporary result of the pandemic to a way of life. However, peace and quiet are hard to come by. To get rid of distraction, Active Noise Control (ANC) particularly the ANC headphones are widely used to attenuate the undesired noise in daily life. While it works, the prolonged usage of headphone can cause some discomfort. This work describes a wireless ANC system implementation designed for the headrest to reduce the noise distraction while doing away with discomfort caused by prolonged usage of headphone. Wireless microphones are utilized to pick up the noise with a high reference-to-interference ratio, which helps in improving the noise reduction performance of the ANC headrest. This paper investigates the feedforward, feedback, and hybrid ANC structures' noise reduction performances in the ANC headrest. Comparative experimental results shown in this paper indicate the effectiveness of the proposed approach in reducing noise around the headrest.

Hybrid feedback active noise control headset based on binaural signal utilization

A standard feedback active noise control (FBANC) headset utilizes the estimate of a primary disturbance at the left-ear (right-ear) error microphone to control noise only at the left (right) ear-cup, i.e., each ear’s controller works independently. In contrast to the FBANC headset, in this paper, a binaural hybrid feedback active noise control (HFBANC) headset is designed that uses the estimate of the primary disturbances at both the left and right-ear error microphones to achieve improved noise control at both the left and right ear-cups. To further improve noise cancellation performance, the nearest Kronecker product decomposition technique is incorporated into the algorithm. The performance of the proposed HFBANC headset is compared to the standard FBANC headset under a variety of different sound field conditions. Experimental results show an improvement of 3–5 dB in the noise cancellation using the proposed algorithms, where the benefits are more prominent for noise sources originating from the side of the user (left and right).

A hybrid multi-reference subband control strategy for active noise control headphones

Multi-reference feedforward Active Noise Control (ANC) has been shown to provide increased noise mitigation capability in ANC headphones compared to a single reference feedforward controllers, due to the availability of additional time advanced information. Recent studies have also shown that the integration of a single-reference feedforward controller and a feedback controller increases both the noise cancellation bandwidth and system stability, therefore, a corresponding hybrid version of the multi-reference control strategy has been developed and is presented in this paper. The hybrid multi-reference ANC (HMRANC) system is comprised of a multi-reference feedforward control unit acting in conjunction with a feedback ANC system. In addition, to overcome the high computational demand of the hybrid structure, a delayless subband version of the HMRANC system is also presented in this paper. The proposed HMRANC and delayless subband HMRANC headphone systems achieve an improvement of 5–10 dB over the previously proposed hybrid ANC headphone system for different acoustic field scenarios. Moreover, the delayless subband HMRANC headphone system is 40% less computationally demanding then HMRANC and 10% less demanding than HANC headphone systems.

A new feedforward and feedback hybrid active noise control system for excavator interior noise

Excavator interior noise is composed of the engine order narrowband noise and the impulsive broadband noise caused by working device. In situations where only narrowband reference signal can be obtained, the feedforward active noise control (ANC) system can just eliminate the narrowband component, failing to control the broadband noise. Hybrid feedforward and feedback ANC (HANC) systems are useful for such applications where the reference signals can be partially obtained. However, existing HANC systems have the following drawbacks: coupling between subsystems; inaccurate error and reference signals; high computational complexity; performance degradation in response to the impulsive noise. To solve the above problems, this paper proposes a new feedforward and feedback HANC system consisting of 3 subsystems, namely, a delayed feedforward narrowband subsystem, a robust feedback broadband subsystem, and a supporting error calculating (SEC) subsystem. In the proposed HANC system, not only subsystems are decoupled, but also accurate error and reference signals are provided by SEC subsystem. In addition, calculating efficiency is improved by applying the delayed notch least mean square (DLMS) algorithm to the feedforward subsystem, and the system robustness is promoted by adopting the robust algorithm into the feedback subsystem. Numerous computer simulations are performed on the excavator interior noise measured under actual operating conditions. The corresponding results demonstrate the superior performance of the proposed HANC system in terms of the power spectral density of residual error, average noise reduction, robustness of the system, and calculation efficiency to the existing HANC systems. Real-time experiments on excavator interior noise showed that the proposed HANC system was efficient in eliminating broadband and narrowband mixture noise in practice.

Robust parallel virtual sensing method for feedback active noise control in a headrest

Additional filter method (AFM) and remote microphone method (RMM) are the two common virtual sensing methods used to compensate for active noise control (ANC) in headrest systems where mounting the error microphone at the desired zone is inconvenient. AFM and RMM are divided into two stages: the training and control stages. In the training stage, a temporary microphone is installed at the target to generate the additional and observation filters for AFM and RMM, respectively. However, they are fully effective only when the disturbances are consistent during both the training and control stages. To solve this dilemma, we propose a robust parallel virtual sensing method (PVSM) for multichannel adaptive feedback ANC system to attenuate the varying narrowband noises. During the training stage, the potential primary noises are used to model a group of parallel additional filters. Subsequently, the synthetic observation filter required for estimating the virtual error signal is derived using minimax optimisation. In the control stage, the most eligible additional filter that minimises the energy of the estimated virtual error signal is matched per frame based on the minimum-mean-square-estimated-error-matching mechanism and applied to the adaptive controller. Theoretical analysis validates that when the primary source varies, PVSM is more robust than both AFM and RMM, and performs noise reduction similar to the direct control with the temporary error microphone placed at the target. The proposed PVSM is further analysed in terms of its convergence condition and computational complexity. Numerous simulations and real-time experiments conducted using a dual-channel ANC headrest validate the feasibility of the proposed algorithm and present a guideline for the selection of frame length in PVSM. Therefore, the proposed PVSM can be considered a robust virtual sensing method against varying primary disturbances.

Differential Error Feedback Active Noise Control With the Auxiliary Filter Based Mapping Method

This letter proposes a differential error feedback active noise control (FBANC) system in an open end duct to mitigate interferences from its downstream. The interference waves entering the duct cause disturbance to the conventional FBANC controller and even result in divergence of the control filter. This is due to the omni-directivity of the error microphone. The differential microphone array (DMA) can be constructed in a compact size using only one more omni-directional microphone. The DMA forms a frequency-invariant beampattern that presents configurable nulls. When the output of the DMA is used as the error signal, the null can be designed to enhance the robustness of the FBANC system against interferences. However, this differential error signal is converted from the sound pressure gradient instead of the sound pressure. The DMA’s location does not precisely indicate the control point where optimum noise reduction has been achieved. To solve this problem, an auxiliary filter based mapping (AFMap) method is developed to map the differential error signal to the location of an omni-directional microphone in the DMA. Experiment results demonstrate that the proposed differential error FBANC system is much less sensitive to interferences than the conventional FBANC system, and the AFMap method can ensure optimum noise reduction occurring at the target control point.

A Robust Feedback Active Noise Control System With Online Secondary-Path Modeling

In this letter, a robust feedback active noise control (FBANC) system with an online secondary-path modeling (OSPM) subsystem is proposed for suppressing sinusoidal noise signals. First, a decoupling scheme is proposed by using of an adaptive linear prediction filter (LPF) that takes the FBANC residual noise as its input and separates the remaining sinusoidal noise from the broadband noise that includes an inherent additive noise within the primary noise as well as a broadband component due to an auxiliary white Gaussian noise (AWGN). The remaining sinusoidal noise (LPF output) is used effectively not only to update the FIR controller but also to scale the AWGN, while the broadband noise (LPF error) serves as a desired signal for the OSPM. In this way, the hostile coupling between the FIR controller and the OSPM subsystem is significantly mitigated, leading to both faster system convergence and improved noise reduction performance (NRP). Second, a simple and practical scheme for refreshing the proposed FBANC system is also developed to enhance its robustness against even abrupt changes with the SP and/or the primary noise signal. Extensive simulations with synthetic and real secondary paths and noise signals are conducted to demonstrate the effectiveness of the proposed FBANC system.

Improved stability performance of a feedback active noise control using a Steiglitz-McBride adaptive notch filter and robust secondary path identification based on variable step size Griffiths LMS algorithm

A stable feedback active noise control (FBANC) system with an improved performance in a broadband disturbance environment is proposed in this article. This is achieved by using a Steiglitz-McBride adaptive notch filter (SM-ANF) and robust secondary path identification (SPI) both based on variable step size Griffiths least mean square (LMS) algorithm. The broadband disturbance severely affects not only FBANC input synthesized but also the SPI.TheSM-ANFestimated signal has narrowband component that is utilized for the FBANC input synthesis. Further, the SM-ANF error has broadband component utilized to get the desired signal for SPI. The use of variable step size Griffiths gradient LMS algorithm for SPI enables the removal of broadband disturbance and non-stationary disturbance from the available desired signal for better SPI. For a narrowband noise field, the proposed FBANC improves the convergence rate significantly (20 times) and the noise reduction from 10 dB to 15 dB (50%improvement) over the conventional FBANC (without SM-ANF and variable step size Griffiths LMS adaptation for SPI).

Narrowband feedback active noise control systems with secondary path modeling using gain-controlled additive random noise

This paper investigates estimation of the secondary path (SP) during the online operation of the filtered-x least mean square (FxLMS) algorithm-based feedback active noise control (FBANC) systems. The proposed method develops upon a previous work where two adaptive filters were used, one for active noise control (ANC) and the other for secondary path modeling (SPM). The proposed method essentially comprises a similar structure as that of the previous method. The objectives here are to suggest modifications to improve upon the slow convergence of SPM filter and the noise reduction (NR) performance in the previous method. The key idea is to employ a gain-controlled modeling signal (generated from the additive random noise signal) mixed with the cancellation signal. The gain-factor for the modeling signal is adjusted such that a large-level modeling signal is used during the transient state of the ANC system. This improves the converge of the SPM filter. As the ANC system converges, the level of the modeling signal is reduced to achieve good NR performance. Besides controlling the level of the modeling, the gain control parameter is employed in adjusting the various other parameters too, viz. fixed step-size, regularization parameter, convergence monitoring parameter, while computing the time-varying normalized step-size for the SPM filter. The simulation results demonstrate that the proposed method (equipped with the proposed modifications) outperforms the previous method and yet with a negligible increase in the computational complexity.

Multirate Audio-Integrated Feedback Active Noise Control Systems Using Decimated-Band Adaptive Filters for Reducing Narrowband Noises.

Audio-integrated feedback active noise control (AFANC) systems deliver wideband audio signals and cancel low frequency narrowband noises simultaneously. The conventional AFANC system uses single-rate processing with fullband adaptive active noise control (ANC) filter for generating anti-noise signal and fullband audio cancelation filter for audio-interference cancelation. The conventional system requires a high sampling rate for audio processing. Thus, the fullband adaptive filters require long filter lengths, resulting in high computational complexity and impracticality in real-time system. This paper proposes a multirate AFANC system using decimated-band adaptive filters (DAFs) to decrease the required filter lengths. The decimated-band adaptive ANC filter is updated by the proposed decimated filtered-X least mean square (FXLMS) algorithm, and the decimated-band audio cancelation filter can be obtained by the proposed on-line and off-line decimated secondary-path modeling algorithms. The computational complexity can be decreased significantly in the proposed AFANC system with good enough noise reduction and fast convergence speed, which were verified in the analysis and computer simulations. The proposed AFANC system was implemented for an active headrest system, and the real-time performances were tested in real-time experiments.

Active noise control for centrifugal and axial fans

Fans are widely used in industry for heat dissipation or airflow production. It is achieved by driving a motor of fan to rotate a number of blades. Most industrial fans can be categorized into one of two general types: centrifugal fans and axial fans. However, fan noise is loud when the motor speed is high. This article develops using active noise control (ANC) system to reduce noise from both centrifugal and axial fans. By integrating loudspeakers and microphones, we present multiple-channel feedback ANC structure with the filtered-X least mean square (FXLMS) algorithm to simultaneously reduce noise from the inlet and the outlet of the fans. Several realtime experiments verify that the proposed method and experimental setup not only reduces the narrowband noise but also achieves the global cancellation of the fan noise.

Alternative switching hybrid ANC

The feedforward active noise control (FF ANC) technique has been widely used to cancel the broadband noise in many practical applications. However, it fails to cope with the uncorrelated narrow-band disturbance picked up by the error sensor, which is independent of the reference signal picked up by the reference sensor. Hence, the alternative switching hybrid ANC has been proposed in the paper to attenuate this kind of uncorrelated narrow-band disturbance effectively. Furthermore, compared with the conventional hybrid ANC algorithms, the proposed algorithm alternatively switches to do updating between the feedforward (FF) and feedback (FB) ANC. The algorithm, thus, saves the computations and achieves a faster convergence speed. The numerical simulations and real-time experiments in the paper validate the effectiveness of the proposed algorithm, which exhibits better convergence and noise reduction performance than other hybrid active control algorithms.