An online decoupling-whitening frequency domain filtered-error least mean square algorithm for active road noise control.

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

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.

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

In practical active noise control (ANC) systems, the primary path and the secondary path may be nonlinear and time-varying. It has been reported that the linear techniques used to control such ANC systems exhibit degradation in performance. In addition, the actuators of an ANC system very often have nonminimum-phase response. A linear controller under such situations yields poor performance. A novel functional link artificial neural network (FLANN)-based simultaneous perturbation stochastic approximation (SPSA) algorithm, which functions as a nonlinear mode-free (MF) controller, is proposed in this paper. Computer simulations have been carried out to demonstrate that the proposed algorithm outperforms the standard filtered-x least mean square (FXLMS) algorithm, and performs better than the recently proposed filtered-s least mean square (FSLMS) algorithm when the secondary path is time-varying. This observation implies that the SPSA-based MF controller can eliminate the need of the modeling of the secondary path for the ANC system.

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

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

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.

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

Commercial active noise and vibration control systems for vehicles are gradually developing, mainly in Japan, for both active noise control of interior sound and active control engine mounts [1]. There have been four Japanese commercial applications: • Active control for booming noise by Nissan in 1991, • Active vibration mount for idle noise and vibration by Toyota in 1997, • Active vibration mount for diesel engines by Nissan in 1998, • Active control for low-frequency road noise by Honda in 2000. Other manufacturers have, of course, developed similar systems, but these have not progressed to production applications. Additionally, active control engine mufflers using cancelling loudspeakers have been successfully demonstrated. A new type of active muffler, using a controlled oscillating butterfly valve in the exhaust line has been developed recently. The future of active control in vehicles is dependent on customers who are willing to pay the higher price for additional noise and vibration control, so that cost reductions are the essential factor in further commercial products. Trends towards fuel efficiency and lighter vehicles run counter to reducing noise and vibration. This has given new interest in active noise control and active control engine mounts as a solution to these conflicting issues.

Active control of road noise considering the vibro-acoustic transfer path of a passenger car

An enhanced multiple-input multiple-output (MIMO) filtered-x least mean square (FXLMS) algorithm using improved virtual secondary path is proposed as the basis for an active noise control (ANC) system for treating vehicle powertrain noise. This new algorithm is developed to overcome the limitation caused by the frequency-dependent property of the standard FXLMS algorithm and to reduce the variation of convergence speed inherent in multiple-channel cases, in order to improve the overall performance of the control system. In this study, the convergence property of the proposed algorithm is analyzed in the frequency domain in order to yield a better understanding of the physical meaning of the virtual secondary path. In practice, because of the arrangement and sensitivities of the actuators (speakers), transducers (microphones), and physical environment, the magnitude response of the main secondary paths can be very different from each other. This difference will cause difficulty in the overall convergence of the algorithm, which will result in minimal attenuation at some of the channels. The proposed channel equalized (CE) virtual secondary path algorithm is designed to tackle this difficulty by equalizing the mean magnitude level of the main secondary paths and by adjusting other secondary paths correspondingly to keep the coupling effects among the control channels unchanged. The performance of the proposed algorithm is validated by analyzing a two-input two-output active powertrain noise control system.

Global active noise control (ANC) systems reduce noise over the entire car cabin with robust performance even as the human head moves; however, they have not been implemented in real-world applications. A robust error sensing strategy is proposed in this paper that is based on which a feasible global ANC system is realized in an electric car, and real-time ANC experiments demonstrate its effectiveness. Simulations based on measured road noise show that using evenly distributed error sensors is a robust error sensing strategy for different car speeds and the upper limit frequency of 3 dB global control is inversely proportional to the equivalent distance between error sensors. Real-time experiments with a 5-seat A-class sedan demonstrate that an average noise reduction of 3.8 dB was achieved between 50 and 250 Hz with 12 evenly distributed error sensors using the four standard car audio loudspeakers as secondary sources. Virtual sensing techniques can be integrated to constitute a more practical system without obstructing the movement of human heads. The findings in this study establish a benchmark for global noise control in car cabins and can be a starting point for future optimization of the system and implementation of adaptive algorithms.

This paper proposes a method for shortening the distance from a noise detection microphone to a loudspeaker forming the feedforward type of active noise control (ANC) system. In the ANC system, the primary noise detected by the noise detection microphone and the secondary noise provided by the loudspeaker is required simultaneously arriving at another microphone, called error microphone, monitoring the effect of the active noise control. However, the reverberation involved in the secondary path from the loudspeaker to the error microphone increase the secondary noise components arriving later than the primary noise. The late components are not only invalid for cancelling the primary noise but also impede the cancellation. This paper hence proposes a method reducing the components by forming the noise control filter synthesizing the secondary noise with the cascade connection of an infinite and a finite impulse response filters. The distance can be thereby shortened.

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.

This paper presents the design of quiet-comfort headboard that uses an active noise control (ANC) system to reduce low-frequency environmental and snoring noises, thus enables the consumers to sleep in a serene environment. The ANC system uses adaptive feedback ANC algorithm with error microphones and loudspeakers mounted on the headboard. MATLAB simulations using the measured transfer functions from the experimental setup are used for designing adaptive algorithm. Real-time experiments using a floating-point digital signal processor demonstrate the designed headboard is effective for reducing snore and other undesired noises.