Adaptive Active Noise Control Algorithm Research Articles

Frequency domain adaptation of multichannel active road-noise sound control system with virtual sensing

The convergence speed of multichannel adaptive active noise control algorithms can be improved by pre-whitening the reference signals and reducing their cross-correlations. When considering nonstationary processes, like road noise, it proves advantageous to use a short time-based cross-correlation estimation of the reference signal, which can be obtained as part of the bin-normalised frequency domain least-means-squares algorithm. However, the regularisation of such an algorithm is crucial to its success and can further improve the convergence rate. In addition, virtual sensors can be employed by using the additional filter method, which provides targets for the measured error signals to follow, generated from the reference signals. Still, the performance of this virtual sensing is known to be degraded if the properties of the reference signals change, so special consideration should be taken when the reference signals are nonstationary.

Selective preconditioning of an active sound control system with virtual sensing

The convergence speed of multichannel adaptive active noise control algorithms can be improved if the reference signals are preconditioned, so it is advantageous to whiten them and reduce their cross-correlations. However, this preconditioning depends on the properties of the reference signals, which will generally change over time. It may still be possible to approximate the properties of the measured reference signals using a limited number of exemplar cases. By classifying the measured reference signals into the closest of these cases, the most appropriate preconditioning can be selected for the adaptive algorithm at any one particular time. Also, one method of implementing virtual sensors is to use the additional filter method, which provides targets for the measured error signals to follow, generated from the reference signals. The performance of this virtual sensing is known to be degraded if the properties of the reference signals change, so this classification method could also be used to select the most appropriate additional filter.

Two-gradient direction FXLMS: An adaptive active noise control algorithm with output constraint

Abstract Active noise control (ANC) is broadly used to cancel the unwanted disturbance in different fields because of its excellent performance in abating low-frequency noise. In practice, however, the limited driving capability of actuators restrict the maximum output power of ANC systems. Once the driving signal of the ANC system exceeds these limitations, the inherent nonlinearity of the actuators will deteriorate the noise reduction and may result in the divergence of the adaptive algorithm. Hence, the two-gradient direction filtered-x least mean square (2GD-FXLMS) algorithm based on the optimal Kuhn-Tucker solution with the output constraint is proposed in this paper. This algorithm has the advantage of minimizing system overdriving, maintaining a specified power budget, and enhancing system stability. Compared to existing output-constrained adaptive algorithms, this proposed algorithm has the same computational complexity as the conventional FXLMS algorithm, while maintaining a stricter output constraint that minimizes the saturation distortion.

Adaptive nonlinear active noise control algorithm for active headrest with moving error microphones

Nonlinear active noise control (ANC) algorithms are used to control nonlinear noise processes. In this paper, a generalized filter bank based nonlinear ANC algorithm is proposed for active headrest application. The algorithm is used to control the noise at both ears by using two moving error microphones which are mounted on the head. Mounting of microphones can be made using a wearing item such as a hat or band. Due to this, these microphones move along with the head. The adaptive ANC algorithm is shown to be very effective for head-movement. Real-time implementation of nonlinear ANC algorithm is made using a dSpace 1104 system for both ears. The robust performance of adaptive nonlinear ANC algorithm is shown for controlling nonlinear noise process at both ears even during head movement using a pair of moving error microphones without re-estimating the secondary paths for the new position.

Robust adaptive algorithm for active control of impulsive noise

Active noise control (ANC) systems employing adaptive filters suffer from stability issues in the presence of impulsive noise. To overcome this limitation, new methods must be investigated. In this paper, we propose the filtered-x state-space recursive least square (FxSSRLS), an SSRLS-based practical and adaptive algorithm for ANC. Computer simulations are executed to verify the enhanced performance of the FxSSRLS algorithm. Symmetric α-stable (SαS) distributions are used to model impulsive noise. The results show that the proposed FxSSRLS algorithm is more robust in eliminating high-peaked impulses than the recently reported algorithms for ANC applications. Moreover, the suggested solution exhibits better stability and faster convergence, without jeopardizing the performance of the proposed solution in terms of residual noise suppression in the presence of impulses.

Recent Patents on Vehicle Interior Active Noise Control Apparatus and Method

With the advancement of digital signal technology, active noise control technique has gradually received more attention for the low-frequency noise in the vehicle. The active noise control apparatus and method have shortcomings in the theory and algorithm. Both acoustic devices researched on meeting the application requirements and adaptive active noise control algorithms are effective ways to improve the practicability of active noise control technique. The paper reviews various vehicle interior active noise control apparatuses in recent years. It has been found that the vehicle interior active noise control apparatuses can be classified as the extracting signal sources and the control strategy between cancelling signal and feedback signal. From review of the patents presented, it is found that there are many patents covering the active noise control method inside vehicle. However, there is a limitation in controlling speed and precision. Therefore, it is recommended that the development should be focused on the design of the control section of vehicle interior noise, which will improve the sound quality and save the space. Moreover, the cost of the new apparatus will be reduced by introducing intelligent controller. Keywords: Acoustic devices, active noise control strategy, algorithm, cancelling signal, feedback signal, vehicle interior noise.

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.

Adaptive active control of free space acoustic noise

This paper concerns adaptive active control of acoustic noise in free space. Traditional adaptive active noise control algorithms are efficient in acoustic ducts; however, they are very unstable and sensitive when being used in free space. An efficient adaptive active noise control algorithm for free space noise is derived based on a root locus analysis on the adaptation process performed in adaptive active noise control. The traditional algorithm and the proposed algorithm are fully implemented by using a high performance embedded controller. The controller is then used for active control of acoustic noise in a duct and, also, in free space. Different experiments show that the traditional active noise control algorithm is not stable when the setup is used in free space. However, the proposed algorithm is stable and converges at a high convergence rate until reaching steady state conditions.

순환형 신경망을 이용한 적응형 능동소음제어시스템의 성능 향상에 대한 연구

The performance of noise attenuation of the adaptive active noise control algorithm is improved using the recurrent neural network. The FXLMS that has been frequently used in the active noise control is simple and has low computational load, but this method is weak to nonlinearity of the main or secondary path since it is based on the FIR linear filter method. In this paper, the recurrent neural network filter has been developed and applied to improvement of the active noise attenuation by simulation.

Direct adaptive active noise control algorithms in case of uncertain secondary path dynamics

AbstractAlmost all adaptive feedforward control algorithms for active noise control require an identified model of the secondary path dynamics from the control loudspeaker to the error and reference microphones. This paper is concerned with new direct adaptive algorithms which will be applicable when the secondary path dynamics are unknown or uncertainly changeable. In the first approach, the control algorithm consists of a robust adaptive feedforward control algorithm and robust feedback control scheme permitting bounded uncertainty in the nominal secondary path model. The second one is a fully direct adaptive feedforward control algorithm which does not require explicit identification of the secondary path dynamics even when all of the path dynamics are uncertain. Simulation and experimental results validate the effectiveness of the proposed algorithms. Copyright © 2005 John Wiley & Sons, Ltd.

Adaptive active noise control algorithms for changes of secondary path dynamics

This paper is concerned with adaptive feedforward control for active noise cancellation. A new LMS type of multi-channel adaptive algorithm is presented for updating the feedforward controllers, which can assure the stability of the adaptive system on the assumption that the secondary path channel is known. Furthermore, an identification-based algorithm is also investigated to deal with uncertainties in the secondary path dynamics. Their validity is examined in simulations and experiments using an air duct.