Nonlinear active noise control using adaptive rational functions with second-order poles frames: Simulation and experimental results

The capabilities of wavelet networks in function approximation make them appealing for black box system identification. In this paper, a new active noise control (ANC) algorithm is developed based on adaptive wavelet networks. The proposed adaptive nonlinear noise control approach employs frames from POLYnominal WinOwed with Gaussian wavelets. Also, a novel network structure for active noise control is derived incorporating a nonlinear static mapping cascaded with an infinite impulse response filter to model the dynamic part of the network. Online dynamic backpropagation learning algorithms based on gradient descent method are applied to adjust the network parameters. Local convergence of the closed-loop system is proved using discrete Lyapunov function.The performance of the proposed ANC system is examined for typical linear/nonlinear cases. The simulation results demonstrate superior performance of this method in terms of stability, fast convergence rate and noise attenuation while avoiding curse of dimensionality.

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

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

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

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) 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 presents a filtered-s Lyapunov algorithm for nonlinear active noise control (NANC) using the filter blank implementation based on functional link artificial neural network (FLANN) filter. First, a Lyapunov function of the tracking error is defined, and adaptive FLANN filter coefficients are then adaptive adjusted based Lyapunov stability theory so that the error converges to zero asymptotically. Analysis of theory and simulation results both show that it has fast error convergence and lower stable state error properties. Moreover, its stability is guaranteed by Lyapunov stability theory.

This paper introduces a reduced Gaussian kernel filtered-x least mean square (RGKxLMS) algorithm for a nonlinear active noise control (NANC) system. This algorithm addresses the computational and storage challenges posed by the traditional kernel (i.e., KFxLMS) algorithm. Then, we analyze the mean weight behavior and computational complexity of the RGKxLMS, demonstrating its reduced complexity compared to existing kernel filtering methods and its mean stable performance. To further enhance noise reduction, we also develop the historical error correction RGKxLMS (HECRGKxLMS) algorithm, incorporating historical error information. Finally, the effectiveness of the proposed algorithms is validated, using Lorenz chaotic noise, non-stationary noise environments, and factory noise.

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.

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.

The paper proposes an algorithm for active noise control (ANC) using the least squares lattice (LSL) algorithm. As a main algorithm for ANC, the LMS algorithm has been used for its simplicity. However, the LMS algorithm has problems of both convergence speed and estimation accuracy in the case of the broadband noise such as road noise which is one of the passenger compartment noises in a car running on rough roads. In order to solve these problems, the LSL algorithm for ANC is considered. By computer simulations, the LSL algorithm turns out to be more effective than the LMS algorithm in both convergence speed and estimation accuracy, especially for the colored input signal used in ANC of road noise.

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

An algorithm for multichannel adaptive IIR (infinite impulse response) filtering is presented and applied to the active control of broadband random noise in a small reverberant room. Assuming complete knowledge of the primary noise, the theoretically optimal reductions of acoustic energy are initially calculated by means of a frequency-domain model. These results are contrasted with results of a causality constrained theoretical time-domain optimization which are then compared with experimental results, the latter two results showing good agreement. The experimental performances of adaptive multichannel FIR (finite impulse response) and IIR filters are then compared for a four-secondary-source, eight-error microphone active control system, and it is found that for the present application FIR filters are sufficient when the primary noise source is a loudspeaker. Some experiments are then presented with the primary noise field generated by a panel excited by a loudspeaker in an adjoining room. These results show that far better performances are provided by IIR and FIR filters when the primary source has a lightly damped dynamic behavior which the active controller must model.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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.

Development of equation-error adaptive IIR-filter-based active noise control system