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

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

A narrowband active vibration control algorithm with frequency estimator based on lattice notch filter is proposed to suppress the primary signal with multi line spectra. A adaptive iir (infinite impulse response) lattice gradient notch filter was adapted to estimated the frequency of the multi-harmonics buried in the primary signal, then the reference signal is filtered by the adaptive band pass filter. With the reference signal filtered by the adaptive band pass filter, active control algorithm is implemented. Simulations of the frequency estimation of the multi-sinusoidal signal are conducted and simulation results confirm that the proposed algorithm could achieve effective performance when the frequency of the primary disturbance is unknown. Active control of vibration has finds a lot application such as fans, engines, and air conditioners in nowadays. The basic concept of active vibration control is to suppress the primary disturbance with the secondary force which is of same amplitude and opposite phase. Signals generated by the rotating machines such as fans, diesel engines, and air conditioners is characterized with discreet frequency which cause damage to operator in the working environment. Narrowband active vibration control (NAVC) is an effective method to suppress such vibration with multi-line spectra. The application of NAVC has been implemented taken into applications since 1970's. A lot of algorithms have been proposed for suppress the primary disturbance with multi-spectral. There exists two families of algorithm in AVC system, which composed of filtered-X least mean square (FXLMS) algorithm and filtered-X recursive least square algorithm (FXRLS). Due to its simplicity and implementation efficiency, FXLMS algorithm is the most popular adaptive algorithm to suppress the primary disturbance compared to FXRLS algorithms. In the application of NAVC systems, the performance of NAVC systems is directly correlated with the reference signal and the primary signal. NAVC algorithm could not achieve effective performance if the correlation between the primary signal and the reference signal is weak. Frequency estimation of the primary signal is an effective method to adapt to improve the relativity between the reference signal and the primary signal. To estimate the frequency of a sinusoidal signal, a number of algorithm has been proposed to exploit the predictable nature of sinusoidal primary disturbance and characterize the frequency of the sinusoidal primary disturbance, such the Kalman algorithm, music, FFT (fast Fourier transform), etc. such method could be capable of tracking nonstationary or slowly-varyin g primary signals. However, they have some shortcomings in the application of NAVC systems. a lot of Kalman-based algorithms have been proposed for the frequency estimation of the sinusoidal signal in many applications such as the electrical power system, however, it requires much more computation burdens which is much more complicated compared the other algorithms. FFT methods have been proposed in the past to estimated the frequency of the primary signal, but it is has been found that the estimated result could not be accessed effectively in real-time of AVC system. Adaptive notch filter has been proposed for the frequency estimation of the sinusoidal signal in a lot of applications. Adaptive lattice filter shows much more stability and performance compared to the bilinear adaptive notch filter. In this paper, the adaptive lattice notch filter is introduced into an adaptive vibration control system to estimate the frequency of synthesized desired signal. Then, the reference signal is generated to suppress the primary vibration in the active vibration control system.

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

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

In this paper, we propose the constrained stability least mean square (CS-LMS) algorithm for multichannel active noise control (ANC). It is shown that the centralized version is able to have better attenuation and tracking performance for different step sizes, ideal and noisy plant models. It is also shown that the proposed distributed version using an incremental communication strategy can obtain a better noise reduction performance is a proper step is used. These performance improvements over the NLMS solutions are obtained with a price of a slight increase in the numerical complexity.

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 survey on active noise control in the past decade–Part II: Nonlinear systems

Active noise control (ANC) algorithms are generally designed to obtain the attenuation of gaussian noise signals using suitable adaptive signal processing algorithms. The rejection of non-gaussian impulsive noise signals represents a much more critical task, with respect to which standard ANC algorithms generally fail to provide a satisfactory solution, due to convergence and instability problems. This paper proposes a novel ANC algorithm for the attenuation of impulsive noise, based on the online estimation of an alpha-stable model of the noise probabilistic description. Simulation analysis shows that the presented approach provides some improvement with respect to competitor methods.

This paper reports on the development of “full-gradient” and “simplified-gradient” versions of the filtered-u algorithm for active noise control. After discussing the general principles of active noise control, the paper presents full mathematical derivations of the full-gradient, simplified-gradient, and Feintuch-based versions of the filtered-u algorithm, clearly showing the various levels of simplifying assumptions that are made along the way. Finally, some illustrative simulation results are presented.

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

This paper describes new algorithms promoting sparsity in modified filtered-x algorithms for active noise control. The proposed algorithms are based on recent techniques incorporating approximations to the to-norm in the cost function used. An approximation to the affine projection algorithm leads to new zero-attracting and reweighted zero-attracting modified filtered-x pseudo affine projection algorithms. The results of simulations indicate that the proposed techniques demonstrate good performance and reduce numerical complexity compared to the original affine projection based algorithms.

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

Impulsive noise reduction is a special problem raised in active noise control (ANC) systems. The filtered-x least mean square (FxLMS) algorithm is a typical ANC method that has been successfully applied in practical applications. Some variants such as the logarithmic FxLMS and modified FxLMS algorithms have been proposed to deal with the impulsive noise for ANC. However, those methods require considerable computational loading in real-time applications. In this paper, a modified median FxLMS method is proposed for its low complexity to avoid the effect of the impulsive noise. Simulation results show that the proposed algorithm has better averaged noise reduction performance than the conventional FxLMS and other order statistics based FxLMS algorithms. In addition, the computational complexity stays the minimum requirement through using only the ordering operation, which is very suitable for the real-time implementation.

An active impulsive noise control algorithm with self-p-normalized method

When developing NVH of a vehicle, identifying a noise transmission vulnerable path is a very important task. The traditional TPA(Transfer Path Analysis) is based on a physical theoretical equation and calculates the contribution of each path by multiplying the ATF(Acoustic Transfer Function) of each path by the driving load. In this case, it is often difficult to calculate the driving load, and M/H consumption is also high. OTPA(Operational Transfer Path Analysis) performs transfer path analysis only with driving signals for each route, but has a disadvantage of insufficient physical causality. In this study, we proposed an efficient indoor noise TPA method that modifies the ANC algorithm by combining the reference signals while driving with the secondary path of Filtered-X LMS. The proposed method uses the secondary transfer function of Fx-LMS as the ATF for each path. It is not necessary to obtain physical load by predicting target noise with the Ref signal for each route. Therefore, it is possible to predict indoor noise with low M/H. In this study, it can be confirmed that the proposed method shows similar results compared to the existing methods and efficiently identifies main noise path for interior noise.