A New Virtual Tracking Sub-Algorithm Based Hybrid Active Control System for Narrowband Noise With Impulsive Interference

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

A novel FELMS-based narrowband active noise control system and its convergence analysis

In this paper we consider active noise control (ANC) of impulsive noise having peaky distribution with heavy tail. Such impulsive noise can be modeled using non-Gaussian stable process for which second order moments do not exist. The most famous filtered-x least mean square (FxLMS) algorithm for ANC systems is based on second order moment of error signal, and hence, becomes unstable for the impulsive noise. Recently we have proposed variants of the FxLMS algorithm where improved performance has been realized either by thresholding the input data or efficiently normalizing the step-size for adaptation. In the practical ANC systems, these thresholding parameters need to be estimated offline and cannot be updated during online operation of ANC systems. Furthermore, normalizing the steps-size for an impulsive noise source would essentially freeze the adaptation for very large impulses. In order to solve these problems, in this paper we propose a novel approach for ANC of impulsive noise sources. The proposed approach is based on data-reusing (DR) type adaptive algorithm. The main idea is to improve the stability by normalizing the step-size, and improve the convergence speed by reusing the data. The computer simulations are carried out to verify the effectiveness of the proposed algorithm.

Based on the theoretical analysis of literature, saturation in measured signal of active noise control (ANC) systems will degrade the convergence speed. However, the experiments show that the saturated input signal can speed up the convergence of the narrow-band ANC systems. This paper intends to remodel the saturation effects for feedforward and feedback ANC systems. Combining the action of analog-to-digital converters (ADC), the mathematical expression and block diagrams are proposed to model the saturation effects in the practical ANC systems. The derivation and simulation results show that since the saturation is able to amplify the principle component of signal, the convergence would be speeded up.

M-estimate based diffusion active noise control algorithm over distributed networks and its performance analysis

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

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

This paper analyzes a narrowband active noise control (ANC) system using the filtered-X least mean-square (FXLMS) algorithm and proposes a method to optimize its performance. The closed-loop transfer function of the narrowband ANC system shows that the undesired out-of-band overshoot is caused by misalignment of the pole and zero as a result of the secondary path. The phase of the internally-generated sinusoidal reference signal can be optimized to compensate for the phase shift of the secondary path, thus minimizing the out-of-band overshoot.

Noise signals generated by rotating machines such as diesel engines, cutting machines, fans, etc., may be modeled as noisy sinusoidal signals which can be successfully suppressed by narrowband active noise control (ANC) systems. In this paper, statistical performance of such a conventional filtered-x LMS (FXLMS)-based narrowband ANC system is investigated in detail. First, difference equations governing the dynamics of the system are derived in terms of convergence of the mean and mean squared estimation errors for the discrete Fourier coefficients (DFCs) of the secondary source. Steady-state expressions for DFC estimation mean square error (MSE) as well as the residual noise power are then developed in closed forms. A stability bound for the FXLMS in the mean sense is also derived. Extensive simulations of various scenarios are performed to demonstrate the validity of the analytical findings.

Online secondary path modeling algorithm without auxiliary noise for narrowband active noise control

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.

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

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.

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.

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.