Hybrid deep learning–based active noise control for encapsulated structures with openings

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

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

For Gaussian noise with random or periodic impulsive interference, the conventional active noise control (ANC) methods with finite second-order moments may fail to converge. Furthermore, the intensity of impulsive noise typically varies over time in the actual application, which also decreases the performance of conventional active impulsive noise control methods. To address these problems, a novel adaptive state detector based post-filtering active control algorithm is proposed. In this work, information entropy with adaptive kernel size is first introduced into the cost function of a post-filtering algorithm to improve its tracking. To enhance the robust performance of adaptive filters when impulsive interference happens, a recursive optimal threshold selecting method is also developed and analyzed by statistical theories. Simulations show that the new method has fast tracking ability in non-impulsive noise environment and keeps robust when impulsive interference happens. It also works well for the impulsive noise of different degrees. Experiment results confirm the effectiveness of the proposed algorithm.

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.

A feedforward active noise control (ANC) system requires a reference signal correlated with the primary disturbance. In situations when such correlated reference signal cannot be obtained, a feedback type ANC may be employed. A hybrid ANC system is combination of feedforward and feedback control, where objective is to simultaneously control both the correlated and uncorrelated primary disturbances. In this paper, we identify problems with the conventional hybrid ANC systems, and propose an improved performance hybrid ANC system. The proposed method introduces third adaptive filter to the conventional hybrid ANC system. The objective is to generate appropriate error and reference signals for feedforward and feedback ANC filters. Computer simulations are carried out which demonstrate that the proposed method can effectively mitigate the correlated and uncorrelated primary disturbances appearing at the error microphone.

In conventional active noise control (ANC) system, the primary noise is attenuated over the frequency band of interest based simply on the error signal, which does not take into account the human perception of noise. Hence, researchers have developed psychoacoustic ANC system to improve its noise reduction performance from psychoacoustic point of view. However, in this psychoacoustic ANC system, there may be a disturbance that is uncorrelated with the primary noise at the error sensor, which can severely degrade the system's noise reduction performance. Hence, in this paper, a psychoacoustic hybrid ANC system is proposed, which can simultaneously control both the correlated primary noise and uncorrelated disturbance from psychoacoustic point of view. Loudness is used as the psychoacoustic criterion for evaluating the system's noise reduction performance. Simulation results show the effectiveness of the proposed psychoacoustic hybrid ANC system.

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.

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

Hybrid noise control is the combination of both active noise and passive noise control. For high-frequency noise, we generally use passive noise control technique, and for low-frequency noise of less than 200 Hz, we use active noise control technique. Passive noise control is achieved by introducing passive techniques such as enclosures, physical barriers, and silencers to attenuate the undesired noise, whereas active noise control (ANC) is achieved by introducing a cancelling “anti-noise” signal generated by a processor or electronics circuit. Digital filters can be used for this purpose with an appropriate adaptive algorithms such as FXLMS, FXNLMS, FBLMS, SE-FXLMS, and FULMS. The development of an ANC simulation model can be divided into two parts: the modelling of primary noise and the modelling of secondary or counter noise. The anti-noise generated by the ANC system can be modelled only if we have the information of primary noise. For successful simulation of secondary noise, we have to model the control system and its interaction with the surrounding acoustical space. This article describes the practical application of active noise control as well as passive noise control to a server room, greatly reducing the level of noise in the whole area through appropriate adaptive algorithms. The results obtained using different algorithms are compared and analyzed.

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.

Interior Acoustic Control System Using Boundary Vibration with Giant Magnetostrictive Actuator: Fundamental Consideration on the Control Algorithm

Mechanical noise is usually a mixture of narrowband and impulsive noise which needs complex active noise control (ANC) algorithms to improve the de-noising performance. But the ANC algorithm with a high computation load will reduce the real-time performance of an ANC system, thus decreasing the attenuation performance and even leading to divergence. To alleviate this contradiction in narrowband ANC systems, a new virtual filtered-x L0 norm discrete Fourier cancellation (FxL0DFC) based hybrid FxNLMS(filtered-x normalized least mean square)-FxDFC framework is proposed to decrease the total computing load and keep good attenuation performance. For a fast-changing noise, the FxNLMS algorithm is employed. The new virtual FxL0DFC algorithm serves to prepare parameters for steady-state, and when this happens, the FxDFC algorithm with the parameters provided by FxL0DFC is applied. Compared to using the FxNLMS algorithm to attenuate narrowband periodical noise, the FxDFC algorithm has nearly the same tracking performance while having a low computational load. As a result, the FxL0DFC-based FxNLMS-FxDFC algorithm leads to a reduction of the total computational load. Moreover, the proposed method performs excellently in terms of tracking in simulations and experiments on actual data, particularly in environments with rapid power changes and impulsive noise.

It is important to remove large environmental noise in measurement using SQUIDs without magnetic shielding. Here, the environmental noise has been reduced by an active noise control (ANC) system in the MHz radio-frequency region. The ANC system including a phase and amplitude control circuit has been designed to allow precise adjustments of the phase and the amplitude of the anti-phase wave against the noise waves in the MHz region. In this paper, a sinusoidal wave in the MHz range was used as the radio frequency interference. When a SQUID was used for a signal detector, the ANC system suppressed this sinusoidal wave to the white noise level by about 54 dB. The performance of the ANC system for the chemical detection by nuclear quadrupole resonance (NQR) was checked in a simulated environment. One was the case of a real AM radio broadcasting signal with the dummy NQR signal and another was the real NQR signal with a sinusoidal wave as noise. For both cases, the ANC showed good performance for MHz range noise cancellation.