Active Noise Control Algorithm Research Articles

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.

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

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.

Diagonal-structure adaptive bilinear filters for multichannel active noise control of nonlinear noise processes

To accommodate multichannel nonlinear active noise control, the second-order Volterra filtered-x LMS (VFXLMS) and filtered-s least mean square (FSLMS) algorithms are usually applied in practical noise control systems. These two algorithms are effective in dealing with polynomial nonlinearity. Recently, a simplified bilinear leaky filtered-x least mean square (SBLFXLMS) algorithm is proposed based on a bilinear model in order to further reduce the computational load but it may have performance degradation. In this paper, we propose a novel nonlinear adaptive algorithm named as the diagonal-structure bilinear filtered-x least mean square (DBFXLMS) algorithm for multichannel nonlinear active noise control. In addition, the developed multichannel DBFXLMS algorithm is equipped with a stability control scheme to maintain a stable condition of the adaptive bilinear filter during the adaptive process. The performance of the proposed algorithm is validated through computer simulations and the computational complexity of the developed algorithm is analyzed. Computer simulations demonstrate that the proposed method has an improvement in terms of control performance in comparison with the conventional first-order FSLMS, second-order VFXLMS, and SBLFXLMS algorithms.

A compact neuromorphic architecture with dynamic routing to efficiently simulate the FXECAP-L algorithm for real-time active noise control

In this work, we introduce, for the first time, the design of a compact neuromorphic architecture to efficiently support a filtered-x error-coded affine projection-like (FXECAP-L) algorithm that is based on affine projection (AP) algorithms for active noise cancellation (ANC) in an acoustic duct. To date, few practical ANC implementations have used AP algorithms because of their high computational complexity, despite providing fast convergence speeds. One of the main factors that increases their computational complexity is linked to the dimensions of the matrix used in the AP algorithm’s computations. Evidently, the largest dimensions of the matrix increase the convergence speed of the AP algorithms by paying a penalty in terms of area consumption. However, convergence speed is crucial in ANC applications since this factor determines the speed at which the noise is canceled. Recently, an FXECAP-L algorithm with evolving order has been proposed to dynamically reduce the dimensions of the matrix by maintaining the convergence speed of AP algorithms. Here, we propose a compact neuromorphic architecture with a dynamic routing mechanism to efficiently implement the evolutionary method of the FXECAP-L algorithm by creating a virtual matrix, whose dimensions can be modified over the filter processing. In this way, we avoid spending a large amount of memory to save the largest matrix elements. In addition, the inclusion of the dynamic routing mechanism in the proposed neuromorphic architecture has allowed us to guarantee low area consumption since the neuromorphic architecture is capable of simulating different adaptive structures without modifying its structure. Here, the neuromorphic architecture has been configured as the system identification and ANC controller for practical noise cancellation in an acoustic duct. Our results have demonstrated that the combination of the properties of the FXECAP-L algorithm and the implementation techniques generate a versatile signal processing development tool that can be used in practical real-time ANC applications.

On Active Impulsive Noise Control (AINC) Systems

This paper develops an efficient adaptive filtering algorithm for active impulsive noise control (AINC) systems. For AINC systems, the filtered-x least mean square (FxLMS) algorithm fails to converge due to the impulsive nature of the noise source. In previous work, the step-size of the FxLMS algorithm was normalized using the power estimate of the error as well as the reference signals, resulting in the improved normalized step-size FxLMS (INSS-FxLMS) algorithm. The INSS-FxLMS algorithm exhibits a robust performance for AINC systems; however, it uses a preselected fixed step-size. Therefore, the INSS-FxLMS algorithm results in a compromise between convergence speed and noise reduction. The proposed algorithm employs a convex-combined step-size (CCSS) within the framework of the INSS-FxLMS algorithm. While normalization takes care of the impulsive nature of noise, the CCSS solves the above-mentioned trade-off issue. Essentially, the CCSS selects a large (small) value of the step-size in the transient (steady) state of the AINC system. It is demonstrated by extensive computer simulations that the proposed algorithm outperforms the existing counterparts for a variety of case studies in AINC systems.

Improving active noise control without secondary path modeling using subband phase estimation

The problem of the secondary path variation on feedforward active noise control (ANC) systems can be solved by applying a phase shift on the reference signal. The existing algorithms choose the phase shift from several fixed candidates by monitoring the residual noise power, which has the problem of deteriorated convergence speed, especially when the difference between the chosen phase shift and the true phase value is large. In this paper, an improved ANC algorithm is proposed by having a better estimation of the phase shift in each subband with the least squares method. Simulation results validate the faster convergence speed of the proposed algorithm, and its computational load is discussed.

Stochastic Analysis of the Filtered-x LMS Algorithm for Active Noise Control

The filtered-x least-mean-square (FxLMS) algorithm has been widely used for the active noise control. A fundamental analysis of the convergence behavior of the FxLMS algorithm, including the transient and steady-state performance, could provide some new insights into the algorithm and can be also helpful for its practical applications, e.g., the choice of the step size. Although many efforts have been devoted to the statistical analysis of the FxLMS algorithm, it was usually assumed that the reference signal is Gaussian or white. However, non-Gaussian and/or non-white processes could be very widespread in practice as well. Moreover, the step-size bound that guarantees both of the mean and mean-square stability of the FxLMS for an arbitrary reference signal and a general secondary path is still not available in the literature. To address these problems, this article presents a comprehensive statistical convergence analysis of the FxLMS algorithm without assuming a specific model for the reference signal. We formulate the mean weight behavior and the mean-square error (MSE) in terms of an augmented weight vector. The covariance matrix of the augmented weight-error vector is then evaluated using the vectorization operation, which makes the analysis easy to follow and suitable for arbitrary input distributions. The stability bound is derived based on the first-order and second-order moments analysis of the FxLMS. Computer simulations confirmed the effectiveness of the proposed theoretical model.

Recursive Least-Squares Lattice Algorithm Combined With Secondary-Path Innovation and Lattice-Order Decision Algorithms for Active Noise Control

This paper proposes a new active noise control (ANC) system based on a recursive least-squares lattice (RLSL) algorithm by designing secondary-path innovation (SPI) and lattice-order decision (LOD) algorithms. The SPI algorithm associated with the power spectral factorization of the secondary path is designed to apply the RLSL algorithm to the ANC system without the filtered-input structure. The SPI algorithm whitens the error microphone signals into the virtual error signals just ahead of the secondary path to construct the virtual desired signals corresponding to the outputs of the lattice filter. The LOD algorithm is designed to operate the ANC system without knowing the tap length of the optimal finite impulse response (FIR) filter. The decision algorithm using the initial and final estimation errors of the lattice filter estimates the tap length of the optimal FIR filter by increasing the lattice order one-by-one until the final estimation error reaches below a threshold value. Even in environments where the tap length of the optimal FIR filter is unknown, the proposed ANC system with low computational complexity shows almost the same performance as the ANC system based on a filtered-input recursive least squares algorithm.

A Realistic Multiple Circular Array System for Active Noise Control Over 3D Space

Spatial active noise control (ANC) systems focus on minimizing unwanted acoustic noise over a continuous spatial region. Conventionally, spatial ANC systems are proposed using point based multi-channel systems and recently novel methods have been developed using spherical harmonic analysis of spatial sound fields. A major limitation for implementing the latter approach is the requirement of regularly distributed microphones and loudspeakers over spherical surfaces. In this paper, we relax the above constraint by constructing a system utilizing multiple circular microphone and loudspeaker arrays and designing a corresponding ANC algorithm. By simulation, we show that the proposed method can achieve comparable ANC performance to conventional spherical array methods. By experiment, we demonstrate the feasibility of implementing the multiple circular array structure and verify its effectiveness given practical constraints.

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.

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.

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.

Dual-Zone Active Noise Control Algorithm

When active noise control (ANC) is applied to acquire a ‘quiet zone’, it may produce an increase in the sound power outside the quiet zone and a change in the primary sound field, which are undesirable in anti-detection and personal audio. To obtain a large noise reduction in the control zone and a small increase of sound power outside the control zone, three wideband ANC algorithms are proposed based on the acoustic contrast control (ACC), least-squares (LS), and least-squares with acoustic contrast control (SFR-ACC) algorithms. With a loudspeaker array as the secondary source, dual-zone ANC with directivity, which realizes noise reduction in one zone without changing the sound power in the other zone, is achieved. Compared with the traditional LS algorithm, the three algorithms proposed in this paper can not only realize that the sound power outside the control zone is increased by less than 1 dB, but also reduce the noise in the control zone by more than 10 dB, which provides a new solution to multi-zone ANC research.

Convergence analysis of the conventional filtered-x affine projection algorithm for active noise control

The conventional filtered-x affine projection (CFxAP) algorithm has been proposed for active noise control due to its potential good convergence and moderate computationally cost. Although some work has been done to analyze the convergence performance of the CFxAP algorithm, they usually adopted relatively strong approximations and hence came to inaccurate results especially at the steady-state. In this paper, we propose a new theoretical model for the CFxAP algorithm to address this problem. The recursion of an augmented weight-error vector is constructed, which is adopted for the mean and mean-square performance analysis of the CFxAP algorithm. Both the correlation between the past weight-error vectors and the dependency of weight-error vectors on past noise vectors are fully considered in our theoretical model. In addition, the treatment does not impose any restriction to the signal distributions. Simulation results show that our theoretical results are more accurate than the previous methods.

Active cancellation of power supply ripple effects in continuous wave superconducting radio frequency cavities

Digital low-level radio-frequency (LLRF) systems are used at the compact energy recovery linac (cERL) test facility to stabilize the accelerating field inside the radio frequency (RF) cavities. Proportional and integral feedback controllers are implemented in the LLRF system to suppress the various disturbances in the cavities. At cERL, the typical disturbances include microphonics detuning and high-voltage power supply (HVPS) ripples. These two types of disturbances are reflected in the sum of sinusoidal fluctuations in the RF phase. Recently, a real-time narrow-band active noise control (ANC) approach which aims to reject the microphonics detuning of less than several dozen Hz, was proved to be effective in cavity resonance control. We extend this ANC method to the LLRF field control to suppress the RF ripples caused by the HVPS. In this paper, we present our experience of applying the ANC algorithm to the LLRF system of the cERL facility at KEK. We confirmed that high frequency ripples of up to 20 kHz can be well compensated by the ANC method during the cERL beam-commissioning.

Less complex solutions for active noise control of impulsive noise

All adaptive algorithms suffer stability issues when employed for the impulsive noise control under the domain of active noise control (ANC) systems. There is a dire need of investigations to overcome this limitation for the impulsive noise, a robust adaptive algorithm is proposed in literature. In the first part of paper, this robust adaptive algorithm is tested for the first time under ANC environment for impulsive noise cancellation and thus, a new ANC algorithm named filtered-x least cosine hyperbolic (FxLCH) algorithm is presented. Simulations are carried out to validate the improved performance of proposed FxLCH algorithm where the impulsive noise realizations are generated by symmetric α-stable distributions. Moreover, the proposed solutions perform better than the standard filtered-x least mean square (FxLMS) algorithm including its variants, and it shows better stability and converges faster than its competitors. Robustness of the algorithm is a constraint in the presence of high impulsive noise. To overcome this problem and to enhance the robustness of proposed FxLCH algorithm, two modifications are suggested. First proposed modification clips the reference and error signals (CFxLCH algorithm), while the second modification integrates already reported normalized step size with FxLCH (MFxLCH) algorithm. The performance of suggested MFxLCH algorithm is validated by extensive simulations. The results exhibited that MFxLCH algorithm acts as a trade-off between FxLMS and filtered-x recursive least square (FxRLS) family algorithms. It has shown better convergence speed than that of FxLMS family algorithms and can approach steady state error as of FxRLS family with almost same computational complexity as of FxLMS family algorithms.

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

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 switching strategy of the frequency-domain adaptive algorithm for active noise control.

The bin-normalized frequency domain block least mean square (NFBLMS) algorithm is a good choice in the active noise control system due to its benefit of high convergence speed. However, it suffers from a biased steady-state solution with insufficient adaptive filter length, often unavoidable due to the influence of the secondary path. A modified frequency domain block least mean square (MFBLMS) algorithm with guaranteed optimal steady-state performance has been proposed recently. However, its convergence speed is generally lower than that of the NFBLMS algorithm. In this paper, a mixed algorithm combining the NFBLMS and MFBLMS algorithms is proposed based on the analysis of the initial convergence trajectory of the NFBLMS algorithm. An effective switching strategy is designed, enabling the MFBLMS algorithm after the NFBLMS algorithm approaches its steady state and shifting back to the NFBLMS algorithm when the change of environment is detected. The mixed algorithm has both the high convergence speed and the optimal steady-state performance, and its effectiveness is validated by simulations using measured acoustic transfer functions.

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.