Adaptive IIR filtered-v algorithms for active noise control

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

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

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.

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

Active noise control (ANC) algorithms are generally designed to obtain the attenuation of gaussian noise signals using suitable adaptive signal processing algorithms. The rejection of non-gaussian impulsive noise signals represents a much more critical task, with respect to which standard ANC algorithms generally fail to provide a satisfactory solution, due to convergence and instability problems. This paper proposes a novel ANC algorithm for the attenuation of impulsive noise, based on the online estimation of an alpha-stable model of the noise probabilistic description. Simulation analysis shows that the presented approach provides some improvement with respect to competitor methods.

This work proposes an improved variable-step FXLMS (IVS-FXLMS) adaptive algorithm for active noise control, which considers the effect of secondary path. A lower bound for the step size is obtained to establish a minimum adaptation-level and a range of step size values for breaking the trade-off between misadjustment and convergence time. Computer simulations are performed for a comparative assessment of the performances of the various adaptive algorithms based on the mean-square-error criteria, with respect to the proposed algorithm, in a high-noise environment. Comparison is done in terms of noise attenuation, reproduction of the original signal and mean square error. It is observed from the obtained results that the FXLMS algorithm, due to its ability to take secondary path into consideration, performs better than the LMS and NLMS adaptive algorithms. However, the proposed algorithm exhibits an improved performance over the FXLMS algorithm by effectively attenuating noise at the output and reproducing the original acoustic signal, which was corrupted with noise. Moreover, the proposed algorithm also incurs the least mean square error of all the considered algorithms.

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.

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.

Active noise suppression for applications where the system response varies with time is a difficult problem. The computation burden for the existing control algorithms with online identification is heavy and easy to cause control system instability. A new active noise control algorithm is proposed in this paper by employing multiple model switching strategy for secondary path varying. The computation is significantly reduced. Firstly, a noise control system modeling method is proposed for duct-like applications. Then a multiple model adaptive control algorithm is proposed with a new multiple model switching strategy based on filter-u least mean square (FULMS) algorithm. Finally, the proposed algorithm was implemented on Texas Instruments digital signal processor (DSP) TMS320F28335 and real time experiments were done to test the proposed algorithm and FULMS algorithm with online identification. Experimental verification tests show that the proposed algorithm is effective with good noise suppression performance.

Adaptive algorithms are described which simultaneously estimate and track the two transfer functions online. No random noise generator is required. Since both transfer functions are estimated, the resulting adaptive algorithms are much more robust than previous ones in that moving the transducer and/or the actuator around does not affect the performance much. Real-time implementation results for one of the algorithms presented are compared with results obtained using the LMS algorithm. It is found that the performance of the LMS algorithm depends greatly on the number of taps used, whereas the new algorithm is almost unaffected by this factor. This demonstrates the robustness of the new algorithm. >

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

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

This paper describes new algorithms promoting sparsity in modified filtered-x algorithms for active noise control. The proposed algorithms are based on recent techniques incorporating approximations to the to-norm in the cost function used. An approximation to the affine projection algorithm leads to new zero-attracting and reweighted zero-attracting modified filtered-x pseudo affine projection algorithms. The results of simulations indicate that the proposed techniques demonstrate good performance and reduce numerical complexity compared to the original affine projection based algorithms.

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.