Adaptive Active Noise Control System Research Articles

Dynamic Matching of Reconstruction and Anti-Aliasing Filters in Adaptive Active Noise Control

Constrained by the computing power, adaptive active noise control systems often have a low sampling rate. Therefore, reconstruction filters and anti-aliasing filters with fixed parameters are generally adopted to eliminate the mirror noise and aliasing noise, respectively; however, they may boost the group delay of the system. A dynamic matching method based on dual sampling rates is proposed to dynamically adjust the parameters of the reconstruction and anti-aliasing filters, according to the characteristics of the primary sound source, for a compromise between high-frequency noise and group delay. In digital high-sampling-rate regions, data that include high-frequency information are analyzed regularly, following which the parameters of the reconstruction filters and those of the anti-aliasing filters are dynamically matched. In digital low-sampling-rate regions, the estimation of the secondary path transfer function is updated. The results of laboratory experiments show that the proposed method not only can suppress the mirror and aliasing noise for primary sound sources with different spectra, but can also effectively reduce the group delay and improve the noise reduction performance of a system.

Variation of the secondary acoustic paths from loudspeakers at the seat shoulder in an automobile cabin

The secondary path is essential in the design of adaptive active noise control systems, and it varies with several factors in practical implementation. In this paper, the acoustic responses from secondary loudspeakers installed at the shoulder of the front passenger seat to the error microphones at the passenger's ears in an automobile cabin are measured, when the factors including the passenger movement, the seat posture and the passenger number change. The upper limit frequency where the phase variation is within 90 deg. compared to the reference position is analyzed. The results show that the state of both the passenger head and the seat back determines the lowest upper limit frequency among all the factors under investigation. If multiple factors change at the same time, the upper limit frequency will be further reduced.

Improving performance of adaptive feedforward noise attenuators using a dynamic adaptation gain

The paper explores in detail the use of dynamic adaptation gain/learning rate (DAG) for improving the performance of adaptive feedforward attenuation schemes. The DAG is an ARMA (poles-zeros) filter embedded in gradient type adaptation/learning algorithms and generalizes the various improved gradient algorithms available in the literature. After introducing the DAG algorithm in the context of adaptive feedforward attenuation schemes and providing relationships with other algorithms, its design is developed. Strictly Positive Real (SPR) conditions play an important role in the design of the DAG. Then the stability issues for adaptive/learning systems using a DAG are discussed. The potential of the DAG is then illustrated by experimental results obtained on a relevant adaptive active noise control system.

A novel adaptive step-size hybrid active noise control system

The conventional hybrid active noise control (HANC) system adopts the standard filtered-x least mean square (FxLMS) algorithm to adapt its control filters. However, the constant step size in the algorithm leads to not only the contradiction between convergence rate and steady-state noise reduction, but inadequate robustness under certain noise circumstances. For performance improvement, a new adaptive step-size method is incorporated into the HANC (ASHANC) system to optimize the adaptation of the filters. It combines the strengths of the variable step-size FxLMS (VSFxLMS) algorithm and the modified normalized FxLMS (MNFxLMS) algorithm. Different step-size adjustment strategies can be obtained by setting specific parameters. Furthermore, to obtain the best performance of the system, we develop a method to determine the involved key parameters on the basis of the improved particle swarm optimization (IPSO) algorithm. The objective function of the IPSO algorithm takes the convergence rate and noise reduction into consideration to search for optimum values. Simulation results demonstrate that the proposed ASHANC system achieves faster convergence rate, better noise reduction, and enhanced robustness than the other investigated systems.

Notes on a New Structure of Active Noise Control Systems

The idea of active noise control is an attenuation of unwanted noise with an additionally generated acoustic wave using the phenomenon of interference. Its technical realization employs advanced control algorithms. Active noise control is an area of intense research and practical engineering applications. In the paper a new structure of adaptive active noise control systems is proposed. Compared with classical control systems used for active noise control, the proposed structure contains in an error signal measurement path an additional discrete-time filter that estimates signal values at the input of this path. These estimates are then used to tune the corresponding adaptive filter. Properties of the proposed adaptive active noise control structure are illustrated by simulation examples in which a feedforward control system equipped with this additional filter is used to attenuate unwanted wide-sense stationary random noises with continuous and mixed spectra.

Online secondary path modeling method with auxiliary noise power scheduling strategy for multi-channel adaptive active noise control system

Accurate model of secondary paths is very crucial for the multi-channel filtered-X least mean square algorithm applied in adaptive active noise control system. The auxiliary random noise technique is popular for online secondary path modeling during adaptive active noise control operation. This paper proposes a simplified variable step-size strategy and an effective auxiliary noise power scheduling strategy for the multi-channel filtered-X least mean square algorithm. Through a defined indirect error signal, the proposed method can guarantee every online secondary path modeling filter has its own exclusive variable step-size strategy to update their coefficients, and every injected noise has its own exclusive scheduling strategy considering all of the corresponding online secondary path modeling filters. The proposed method can improve the adaptive performance and simplifies the complexity of multi-channel adaptive active noise control system. Computer simulations show that the proposed method gives much better noise reduction and secondary path modeling accuracy at a somewhat faster convergence rate comparing with the competing methods.

Development of the real-time adaptive active noise control system based on SAN algorithm

Development of the real-time adaptive active noise control system based on SAN algorithm

Integrated active noise control for open-fit hearing aids with customized filter

The open-fit hearing aids that allow air and sound into the ear canal have become popular in recent years. Unfortunately, they result in the noise leakage problem that bypasses the hearing aids toward the eardrum and will degrade the quality of desired signal. This paper proposes a modified audio-integrated adaptive feedback active noise control (ANC) system for open-fit hearing aids. Traditionally, the ANC system attenuates the total power of the unwanted noise over the entire spectrum. To increase efficiency and save power consumption of hearing aids, we design a customized filter based on the hearing threshold of hearing-impaired patient to generate the anti-noise only in audible frequency range. The performance of the proposed method is verified by both computer simulations and real-time experiments.

A Higher-Order Spectra Based Method of On-Line Secondary Path Model Identification for Active Noise Control Systems

The aim of this paper is to present a method of nonparametric and parametric secondary path model identification for adaptive active noise control systems with low-power non-Gaussian excitations of the form of a higher-order discrete-time multisine random process and data processing based on cross-higher-order spectra. Properties of the discussed method are illustrated by simulation experiments devoted to secondary path identification for feedforward and feedback active noise control systems. Its robustness to nonlinear distortions implied by data acquisition system and adaptation procedure is proved.

Root Locus Analysis of the Adaptation Process in Active Noise Control for Repetitive Impulses

The popular filtered-x least-mean squares (FxLMS) algorithm has been widely adopted in active noise control (ANC) for relatively stationary disturbances. The convergence behavior of the FxLMS algorithm has been well understood in the adaptation process for stationary sinusoidal or stochastic white noises. Its behavior for transient impulses has not received as much attention. This paper employs the root locus theory to develop a graphical tool for the analysis and design of the adaptive ANC system for repetitive impulses. It is found that there is a dominant pole controlling the stability of the adaptation process, in which the maximum step size can be determined. The analysis also observes a transient adaptation behavior in the FxLMS algorithm for repetitive impulses. In this case, the predicted step-size bound decreases as the number of repetitive impulses increases for a general secondary path. Furthermore, the dominant root tuning process is applied by incorporating a digital filter after the output of the adaptive controller, which significantly increases the step-size bound. The accuracy of the analysis was extensively validated by numerical simulation studies by assuming various secondary path models. The simulated results show an excellent agreement with analytical predictions.

Simulation Analysis and Experiment Research on Vehicle Interior Adaptive Active Noise Control System

In the MATLAB/Simulink environment through the simulation analysis, has determined the NFXLMS algorithm of the filter order and the convergence factor control parameters in the vehicle adaptive active noise control system used; adaptive active interior noise control system was constructed in a light bus, the tested vehicle engine was conducted noise cancellation control when its work in 1080rpm and 2400rpm; the results show that, the overall amount of noise reduction of driver's right ear position respectively 9.7dB (Lin) and 8.9dB (Lin).

Adaptive feedback active noise control using wavelet frames: simulation and experimental results

Two strategies for adaptive feedback active noise control (ANC) systems are presented here based on wavelet frames. In the first strategy, the noise at the cancellation point is estimated and used as the reference signal in the feedforward ANC system. In the second strategy only the error signal is utilized in the wavelet-based ANC system. The advantages of the second method are: simple structure and less computational load (no need to estimate noise) as well as less sensitivity to the accuracy of estimated secondary path filter. Its disadvantage is high sensitivity to learning rate. To treat this, an adaptive learning rate is proposed to maintain the stability and convergence rate simultaneously. Simulations are carried out for typical linear/nonlinear cases to compare the proposed methods with FxLMS and neural network-based ANC algorithms. Experiments are then conducted to evaluate the developed algorithms. Results show the superior performance of proposed methods in terms of the convergence rate and noise attenuation especially in the presence of an inaccurate identified secondary path filter.

A simplified adaptive feedback active noise control system

In the adaptive feedback active noise control system based on the internal model control (IMC) structure, the reference signal is regenerated by synthesizing the error signal and the secondary signal filtered with the estimation of the secondary path, hence more computation load and extra programming are required. Motivated by the engineering truth that the primary noise cannot be completely cancelled in most practical active noise control applications and the error signal still contains some portions of the primary noise, a simplified adaptive feedback active noise control system is proposed in this paper, which adopts the error signal directly as the reference signal in an adaptive feedforward control system and utilizes the leaky filtered-x LMS algorithm to update the controller. The convergence properties of the proposed system are investigated and its advantages are discussed by comparing with other feedback control systems as well as the weakness. Finally, simulations and experiments are carried out to demonstrate the effectiveness of the proposed system.

Adaptive signal processing algorithms for creating spatial zones of quiet

Available adaptive active noise control (ANC) algorithms can only minimize the noise level at a point that an error microphone is placed. Consequently, a zone of quiet around this microphone is produced as a byproduct. However, they cannot technically control or, even, monitor the noise level within the zone of quiet unless they use several sensors. They cannot also control the shape and the extension of the quiet zone by using only the error microphone. This paper develops a signal processing framework for the derivation of adaptive ANC algorithms that can directly create a controllable zone of quiet in monochromatic noise fields using a single error microphone. It is shown that by adding a filter to the standard ANC structure, a controllable zone of quiet is created. The transfer function of this filter is obtained using an accurate mathematical analysis. It is also shown that the extension of the zone of quiet can be controlled by tuning this filter. The implementation of the proposed system requires no additional hardware, rather than those required for traditional ANC systems. The validity of the results are discussed by using numerical analysis. Also, the performance of the proposed system is practically verified.

A new online secondary path modelling for feedforward ANC systems

Adaptive filter-based feedforward active noise control (ANC) systems are generally realised in system identification configuration. The secondary path of such ANC systems contains numerous subsystems, whose transfer function needs to be compensated for the ANC systems to work satisfactorily. Different methods are available for online modelling of secondary path and realising ANC system using FxLMS algorithm. Simulation of a few such kinds of existing systems is done using white Gaussian noise (WGN) as primary noise and comparing their performances. A new ANC system with online secondary path modelling is proposed and analysed. Simulation study revealed that the proposed ANC system shows superior performance compared to the existing systems in terms of average power reduction of residual noise and converging time when WGN as primary signal.

Convergence analysis of filtered-x least mean squares algorithm for active control of repetitive impact noise

The prevalent adaptive active noise control (ANC) algorithm, namely, the filtered-x least mean squares (FXLMS), exhibits a critical challenge for treating transient impact noise. This is because the FXLMS algorithm requires certain adaptation time to converge satisfactorily. However, the FXLMS algorithm may have its learning capacity when the transient noise shows certain repeatability. In this paper, a distinctive theoretical analysis of the convergence behavior of ANC system with the standard FXLMS algorithm is conducted for repetitive impulse-induced transient noise control. To simplify the derivation, the secondary path is assumed to be a pure delay model. Through this analysis, a step size bound condition is derived, and an optimal step size that leads to the fastest convergence is determined. To validate the analysis, extensive numerical simulations are performed considering various pure delay secondary path models. Calculations are in very good agreement with the theoretical analysis. Finally, a more general secondary path is considered to further demonstrate the effectiveness of the FXLMS algorithm for repetitive impulse noise control. The results indicate that ANC system with the FXLMS algorithm can be a very promising technique for repetitive transient noise typically seen in industrial facilities such as punching machines.

Simulation of Active Noise Control System on the Truck Interior Cab

Studying on adaptive active noise control (AANC) system of the truck interior cab to reduce the low-frequency noise,a normalization FLMS algorithm simulink model is established in Matlab/Simulink.Then taking it as the core,a feedforward adaptive active control system and a feedback adaptive active control system of the tuck interior cab are established in Matlab/Simulink .Considerating the actual error channels effects on systems ,the noise reduction effects of two adaptive active control systems are verified from the simulintion results.Through comparing the two wo adaptive active control systems,we found that the feedforward adaptive active control system is more stable.

The Study of Fuzzy Control Algorithm in a Duct Active Noise Control System

In this paper, the application of fuzzy control algorithm in duct active noise control technology was studied, according to the problem of secondary acoustical feedback existing in feedforward control structure (FCS) adaptive active noise control (AANC) system, a reverse control structure of fuzzy active noise control (FANC) system was established. Through Matlab software the fuzzy control algorithm was simulated, and the simulation results showed that it has a good convergence and stability in the FANC system.

Research on Filtered-XLMS Algorithm Based Adaptive Active Noise Control

The core of adaptive active noise control system is the adaptive filter and the corresponding adaptive algorithm, the article described the principle of adaptive filtering for active noise control, focus on derivation of Filtered-XLMS algorithm, using MATLAB simulated and implemented Filtered-XLMS algorithm based adaptive active noise control system, and analyzed the filter length and the convergence factor on system performance.

Active Noise Control Based on TMS320C5509

The feed-forward adaptive active noise control (AANC) system is presented. Firstly, the hardware project of the system is brought forward, by selecting TMS320C5509 DSP as the controller. Then, using the mixed language, the active noise real-time control system is realized, based on the FXLMS algorithm. It’s proved that a good noise cancellation is achieved by the experiment.