Active Noise Control Algorithm Research Articles

Intelligent Adaptive Active Noise Control in Non-stationary Noise Environments

The famous filtered-x least mean square (FxLMS) algorithm for active noise control (ANC) systems may become unstable in non-stationary noise environment. To solve this problem, Sun`s algorithm and Akhtar`s algorithm are developed based on modifying the reference signal in update of FxLMS algorithm, but these two algorithms have dissatisfactory stability in dealing with sustaining impulsive noise. In proposed algorithm, probability estimation and zero-crossing rate (ZCR) control are used to improve the stability and performance, at the same time, an optimal parameter selection based on fuzzy system is utilized. Computer simulation results prove the proposed algorithm has faster convergence and better stability in non-stationary noise environment.

A Two-Stage Adaptive Algorithm in the Frequency Domain for a Multichannel Feedforward Active Noise Control System

The reference paths between original sources and reference sensors in multichannel feedforward active noise control (ANC) systems are often ignored by most ANC algorithms. Therefore, a two-stage adaptive algorithm in the frequency domain is proposed to deal with several of the more complicated cases, specifically addressing instances when the reference sensors must be placed far away from the noise sources. This two-stage system includes a blind pre-processing system cascaded with a traditional MFxLMS system; both systems operate in the frequency domain. The new algorithm can eliminate the effect of reference paths by performing blind pre-processing independently. The MFxLMS algorithm in the following stage can achieve faster convergence than the traditional algorithm without blind pre-processing. The computational complexity of the proposed algorithm is analysed and a numerical simulation using impulse responses measured in a real reverberant room is performed to verify the convergence performance of the proposed algorithm. y Member of the International Institute of Acoustics and Vibration (IIAV)

Nonlinear active noise control for headrest using virtual microphone control

In this paper, a feed-forward nonlinear active noise control algorithm is developed using the Filtered-S LMS (FSLMS) algorithm and virtual microphone control for an active headrest application. Virtual microphone control is implemented to attenuate noise at the ears of the headrest occupant using the physical microphones placed at remote locations. The proposed nonlinear virtual microphone control algorithm is verified in real-time in a headrest. Different nonlinear noise processes are studied and it is shown that in all cases, the FSLMS algorithm outperforms the FXLMS algorithm in controlling nonlinear primary noise at a virtual location.

Active noise cancellation algorithms for impulsive noise

Impulsive noise is an important challenge for the practical implementation of active noise control (ANC) systems. The advantages and disadvantages of popular filtered-X least mean square (FXLMS) ANC algorithm and nonlinear filtered-X least mean M-estimate (FXLMM) algorithm are discussed in this paper. A new modified FXLMM algorithm is also proposed to achieve better performance in controlling impulsive noise. Computer simulations and experiments are carried out for all three algorithms and the results are presented and analyzed. The results show that the FXLMM and modified FXLMM algorithms are more robust in suppressing the adverse effect of sudden large amplitude impulses than FXLMS algorithm, and in particular, the proposed modified FXLMM algorithm can achieve better stability without sacrificing the performance of residual noise when encountering impulses.

Performing Active Noise Control and Acoustic Experiments Remotely

This paper presents a novel and advanced remotely controlled laboratory for conducting Active Noise Control (ANC), acoustic and Digital Signal Processing (DSP) experiments. The laboratory facility, recently developed by Blekinge Institute of Technology (BTH) Sweden, supports remote learning through internet covering beginners level such as simple experimental measurements to advanced users and even researchers such as algorithm development and their performance evaluation on DSP. The required software development for ANC algorithms and equipment control are carried out anywhere in the world remotely from an internet-connected client PC using a standard web browser. The paper describes in detail how ANC, acoustic and DSP experiments can be performed remotely The necessary steps involved in an ANC experiment such as validity of ANC, forward path estimation and active control applied to a broad band random noise [0-200Hz] in a ventilation duct will be described in detail. The limitations and challenges such as the forward path and nonlinearities pertinent to the remote laboratory setup will be described for the guidance of the user. Based on the acoustic properties of the ventilation duct some of the possible acoustic experiments such as mode shapes analysis and standing waves analysis etc. will also be discussed in the paper.

Improving speech understanding in communication headsets: Simulation of adaptive subband processing for speech in noise

Speech communication headsets are necessary for many high-noise environments to maintain interaction between individuals and facilitate safe working conditions. However, current hearing protection devices intended to protect hearing health can impede speech communication or expose persons to sound pressure levels (SPLs) that could lead to excessive noise exposure if a communication signal is presented improperly. This paper explores an adaptive subband communication algorithm, based on a delayless subband active noise reduction architecture, intended to adjust the communication channel gain to provide an appropriate speech signal power in relation to the instantaneous environmental noise power. The method monitors SPLs underneath the ear cup of a communication headset to provide a target speech signal-to-noise ratio without exceeding safe noise exposure thresholds. A series of computer simulations derived from a real-world communication headset model are used to compare the method developed with a traditional passive attenuation headset and a commercial active noise reduction design. The simulations demonstrate the ability of the adaptive subband communication algorithm to adjust automatically the speech signal gain for improved intelligibility while maintaining healthy noise exposure levels. Relevance to industryThe electro-acoustic performance of an active speech communication headset is explored by simulation. The concept integrates a subband active noise control algorithm with an adaptive gain control structure to improve speech intelligibility in a noisy environment. The concept automatically selects appropriate communication channel gain levels without exceeding hearing damage thresholds or requiring user input, and is directly applicable to a practical device.

Fractional lower order moment based adaptive algorithms for active noise control of impulsive noise sources

This letter deals with active noise control (ANC) for impulsive noise sources being modeled using non-Gaussian stable process. The filtered-x least mean square algorithm is based on minimization of the variance of the error signal and becomes unstable for impulsive noise. The filtered-x least mean p-power algorithm-based on minimizing the fractional lower order moment-gives a robust performance for impulsive ANC; however, its convergence speed is very slow. This letter proposes modifying and employing a generalized normalized LMP algorithm for impulsive ANC. Extensive simulations are carried out which demonstrate the effectiveness of the proposed algorithm.

An M-Estimator based algorithm for active impulse-like noise control

M-Estimator is a popular technique in robust statistics which targets to reduce the effect of outliers. Several existing algorithms for active impulse-like noise control are reformulated as one species based on M-Estimator respectively. A new M-Estimator based algorithm named Fair algorithm is proposed in the context of active impulse-like noise control. Simulations are performed to verify the effectiveness of the Fair algorithm and it is shown that the proposed algorithm compares favorably with the competitor algorithm in terms of noise control performance, while its computation complexity is relative lower.

Development and convergence analysis of new least‐mean‐square‐based algorithm equipped with exponential‐decay step size and disturbance compensation for active noise control

SUMMARYIn real‐world active noise control (ANC) applications, disturbance can be picked up by error sensors and significantly degrade the steady‐state ANC performance. This study proposes two techniques in combination with a least‐mean‐square (LMS) based ANC algorithm, named normalized filtered‐x LMS/commutation error (NFxLMS/CE) algorithm, to deal with the disturbance that is independent of a reference signal. A new stochastic method to analyze convergence properties of the NFxLMS/CE algorithm under influence of the disturbance is first established. Given that the reference signal is persistently exciting of sufficient order, exponential convergence of the algorithm is derived with a step‐size condition. An exponential‐decay step size (EDSS) is then proposed to obtain a new ANC algorithm referred to as EDSS‐NFxLMS/CE algorithm. In addition, a disturbance‐compensation (DC) technique is developed for the EDSS‐NFxLMS/CE algorithm to obtain an EDSS‐NFxLMS/CE_DC algorithm such that the influence of the disturbance can be reduced. It is shown that the EDSS‐NFxLMS/CE_DC algorithm is exponentially convergent. Moreover, computer simulations show that the EDSS‐NFxLMS/CE_DC algorithm can achieve a better ANC performance in terms of convergence rate and level of noise reduction as compared with that using the EDSS‐NFxLMS/CE algorithm without DC and that using NFxLMS/CE_DC algorithm of constant step sizes. These results support the effectiveness of the proposed techniques and EDSS‐NFxLMS/CE_DC algorithm. Copyright © 2012 John Wiley & Sons, Ltd.

A nonlinear active noise control algorithm for virtual microphones controlling chaotic noise

In active noise control (ANC) systems, virtual microphones provide a means of projecting the zone of quiet away from the physical microphone to a remote location. To date, linear ANC algorithms, such as the filtered-x least mean square (FXLMS) algorithm, have been used with virtual sensing techniques. In this paper, a nonlinear ANC algorithm is developed for a virtual microphone by integrating the remote microphone technique with the filtered-s least mean square (FSLMS) algorithm. The proposed algorithm is evaluated experimentally in the cancellation of chaotic noise in a one-dimensional duct. The secondary paths evaluated experimentally exhibit non-minimum phase response and hence poor performance is obtained with the conventional FXLMS algorithm compared to the proposed FSLMS based algorithm. This is because the latter is capable of predicting the chaotic signal found in many physical processes responsible for noise. In addition, the proposed algorithm is shown to outperform the FXLMS based remote microphone technique under the causality constraint (when the propagation delay of the secondary path is greater than the primary path). A number of experimental results are presented in this paper to compare the performance of the FSLMS algorithm based virtual ANC algorithm with the FXLMS based virtual ANC algorithm.

Time–frequency-domain filtered-x LMS algorithm for active noise control

This paper presents a time–frequency-domain filtered-x LMS (FXLMS) algorithm for active noise control (ANC) based on the short-time Fourier transform (STFT). We show that proposed algorithm has much reduced computational complexity and better convergence performance, as compared with the time-domain FXLMS algorithm. Additionally, computer simulations show that a time–frequency-domain FXLMS algorithm for ANC is effective in canceling non-stationary noise while a frequency-domain FXLMS algorithm remains inadequate at this task.

On family of fractional lower order moment (FLOM)-based algorithms for active noise control of impulsive noise sources

Active noise control (ANC) is based on the principle of destructive interference of propagating acoustic waves; essentially a cancelling signal is generated and combined with the primary noise to achieve acoustic cancellation around location of the error microphone. In this paper, we consider a very challenging application of ANC for impulsive noise. The impulsive noise can be modeled using non-Gaussian stable process for which second order moment does not exist. The most famous filtered-x-LMS (FxLMS) algorithm for ANC systems, is based on minimization of the variance of the error signal, and therefore, becomes unstable for the impulsive noise. It has been shown that filtered-x least mean p-power (FxLMP) algorithm; based on minimizing the fractional lower order moment (FLOM) that does exist for stable distributions; gives robust performance for impulsive ANC. However, the convergence speed of the FxLMP algorithm is very slow. Recently; we have proposed various variants of FxLMP algorithm, so that an improved convergence and noise reduction performance is achieved. In this paper, we propose modifying and employing generalized normalized LMP algorithm (GNLMP) algorithm for ANC of impulsive noise. The computational complexity of proposed algorithm is comparable to the existing FLOM-based ANC algorithms. Extensive simulations are carried out, which demonstrate the effectiveness of proposed algorithm. We observe that, in comparison with the existing FLOM-based ANC algorithms, the proposed algorithm gives best performance for ANC of impulsive noise sources.

Fixed point realization of partial updating adaptive algorithm for active noise control

An efficient low cost realization of the partial updating FXLMS algorithm is proposed in this paper. Unlike the conventional partial updating adaptive filters, the modified sign-LMS algorithm is used to make it easier to be implemented in a fixed point DSP. Moreover, the calculation of the filtered reference signal is divided into several steps, which further reduces the whole computational burden. An active noise controller using TMS320C5502 DSP chip is designed based on the proposed algorithm. The experiment results demonstrate that at a reasonable updating rate, the proposed algorithm has exactly the same performance with the normal float-point FXLMS algorithm while the computational burden is much lower.

A novel frequency-domain periodic active noise control algorithm with nonlinear secondary path

Frequency-domain periodic active noise control (FDPANC) algorithm is usually adopted for harmonic noise reduction. However it may fail to converge when the secondary path includes a nonlinear element. In this paper, the convergence behavior of FDPANC algorithm with nonlinear secondary path is analyzed and a sufficient condition for convergence is derived. Unlike the conventional FDPANC algorithm with linear secondary path, this sufficient condition for FDPANC algorithm with nonlinear secondary path is time-varying and depending on the coefficients of the adaptive filter. If system has a steady-solution, the sufficient condition can be satisfied for all iterations by selecting the proper initial values. In this situation, the adaptive filter will convergent to the optimum value and the disturbance signal can be eliminated totally. If system has no steady-solution, the sufficient condition cannot be satisfied for all iterations whatever the initial values are. Based on the sufficient condition for convergence, a modified FDPANC algorithm integrated with convergence detector is also proposed in this paper.

A robust filtered-s LMS algorithm for nonlinear active noise control

The performance of a nonlinear active noise control (ANC) system based on the recently developed filtered-s least mean square (FsLMS) algorithm deteriorates when strong disturbances in the ANC system are acquired by the microphones. To surmount this shortcoming, a novel robust FsLMS (RFsLMS) algorithm is proposed for a functional link artificial neural network (FLANN) based ANC system. The new ANC system is least sensitive to such disturbances and does not call for any prior information on the noise characteristics. The results obtained from simulation study establish the effectiveness of this new ANC scheme.

Particle Swarm Optimization Based Active Noise Control Algorithm Without Secondary Path Identification

In this paper, particle swarm optimization (PSO) algorithm, which is a nongradient but simple evolutionary computing-type algorithm, is proposed for developing an efficient active noise control (ANC) system. The ANC is conventionally used to control low-frequency acoustic noise by employing a gradient-optimization-based filtered-X least mean square (FXLMS) algorithm. Hence, there is a possibility that the performance of the ANC may be trapped by local minima problem. In addition, the conventional FXLMS algorithm needs prior identification of the secondary path. The proposed PSO-based ANC algorithm does not require the estimation of secondary path transfer function unlike FXLMS algorithm and, hence, is immune to time-varying nature of the secondary path. In this investigation, a small modification is incorporated in the conventional PSO algorithm to develop a conditional reinitialized PSO algorithm to suit to the time-varying plants of the ANC system. Systematic computer simulation studies are carried out to evaluate the performance of the new PSO-based ANC algorithm.

Real-time experiments of ANC systems for infant incubators

This paper presents real-time experiments of applying active noise control (ANC) algorithms for infant incubators to reduce recorded neonatal intensive care unit (NICU) noise. Research shows that 10�15% of newborns spend time in NICU and high noise levels in the infant incubator result in numerous adverse health effects. The ANC system has potential benefits of generating quiet environment for newborns inside the incubators and decreasing the risk of health impairment and disability. Real-time experiments of single-channel and multiple-channel ANC structures were conducted using real recorded NICU noise samples. An average reduction of 13 dB and 17 dB are obtained by using single-channel and multiple-channel ANC systems.

Adjoint nonlinear active noise control algorithm for virtual microphone

Controlling nonlinear noise processes at a virtual location involves higher computational complexity compared to traditional active noise control (ANC) at a physical sensor. In an attempt to reduce the computational burden, a filtered-error LMS based ANC algorithm is proposed in this paper for controlling a nonlinear noise process at a virtual location. The filtered-error based algorithm using the adjoint of the secondary path is used to develop both linear and nonlinear ANC controllers with the later based on the functional link artificial neural network. A computer simulation and real-time experimental study with detailed computational complexity analysis are presented to support the proposed algorithm.

Comparison of Performance and Computational Complexity of Nonlinear Active Noise Control Algorithms

Research on nonlinear active noise control (NANC) revolves around the investigation of the sources of nonlinearity as well as the performance and computational load of the nonlinear algorithms. The nonlinear sources could originate from the noise process, primary and secondary propagation paths, and actuators consisting of loudspeaker, microphone or amplifier. Several NANCs including Volterra filtered-x least mean square (VFXLMS), bilinear filtered-x least mean square (BFXLMS), and filtered-s least mean square (FSLMS) have been utilized to overcome these nonlinearities effects. However, the relative performance and computational complexities of these algorithm in comparison to FXLMS algorithm have not been carefully studied. In this paper, systematic comparisons of the FXLMS against the nonlinear algorithms are evaluated in overcoming various nonlinearity sources. The evaluation of the algorithms performance is standardized in terms of the normalized mean square error while the computational complexity is calculated based on the number of multiplications and additions in a single iteration. Computer simulations show that the performance of the FXLMS is more than 80% of the most effective nonlinear algorithm for each type of nonlinearity sources at the fraction of computational load. The simulation results also suggest that it is more advantageous to use FXLMS for practical implementation of NANC.

Filtered weight FxLMS adaptation algorithm: Analysis, design and implementation

AbstractIn the Filtered‐x Least‐Mean‐Square (FxLMS)‐based Active Noise Control (ANC), the convergence speed of the adaptation process has a direct relationship to a scalar parameter, called the step size. There is a theoretical upper‐bound for the step size beyond which the system becomes unstable. However, the step size is usually set to a number smaller than its upper‐bound in practice. This is because for relatively large step sizes, the adaptation process becomes very sensitive to any non‐stationary change in acoustic noise. Owing to this trade‐off, real‐time implementation of high‐performance ANC systems becomes challenging. To overcome this problem, this paper develops a novel ANC algorithm in which a recursive filter compensates for influences of the step size increase on the system performance. It is shown that this filter can efficiently increase the step size upper‐bound; consequently, the performance of the system is improved. This improvement is demonstrated using computer simulation. Also, experimental results shows the preference of the proposed algorithm to the traditional FxLMS‐based ANC algorithm in practice. Copyright © 2011 John Wiley & Sons, Ltd.