Set-membership Sparsity-Aware Proportionate Normalized Least Mean Square Algorithms for Active Noise Control

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

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.

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

Objectives: To enhance the quality of Cardiac Signal for perfect diagnosis by the doctor. Methods/Statistical Analysis: We are introducing some adaptive filter structures for Cardiac Signal (CS) enhancement for the extraction of high resolution cardiac signals and these structures were based on the Proportionate Normalized Least Mean Square (PNLMS) algorithm. The main advantage of PNLMS over the conventional techniques is extraction of sparse coefficients, suitably weighing them and fast convergence. These ANCs are tested using MIT-BIH database to compare the performance. Findings: We consider Signal to Noise Ratio (SNR), Excess Mean Square Error (EMSE), Misadjustment (MSD), convergence curves and residual error plots as performance measures. Among the ANCs tested, PMNSRLMA based ANC is found to be better with reference to the considered performance measures and computational complexity. The average SNRI achieved by this ANC is 18.8856dBs for PLI elimination, 8.7580dBs for BW elimination, 8.5106dBs for MA elimination and 8.5012dBs for EM elimination. From the above results it is clear that in practical biotelemetry applications to minimize the computational complexity of the noise canceller we combine PNLMS with signature algorithms. Again, to reduce the complexity in the denominator of the normalized recursion, we use maximum normalized version of PNLMS. Finally, these variations result in seven algorithms in addition to PNLMS. Based on these algorithms, we develop various Adaptive Noise Cancellers (ANCs) to eliminate artifacts present in the CS and to present best quality signal to the doctor for diagnosis. Application/ Improvement: The standard of Cardiac Signal can be enhanced by improving data acquisition methods.

SummaryIn this paper, the proportionate normalized least mean square (PNLMS) and its modifications, such as improved PNLMS (IPNLMS) and μ‐law PNLMS (MPNLMS) algorithms, developed for a sparse system, are analyzed for a compressed input signal. This analysis is based on a comparative study of the steady‐state error and convergence time for the original signal and the compressed signal. Further, in this paper, a filter PNLMS (FPNLMS) algorithm that is a modification of the IPNLMS algorithm is proposed. The FPNLMS algorithm uses a step size varying in time to adapt to the sparse system. Simulations are carried out to compare the proposed FPNLMS algorithm for different signal‐to‐noise ratio for a compressed input signal with existing algorithms, ie, PNLMS, MPNLMS, and IPNLMS algorithms. The FPNLMS algorithm achieves a better steady‐state and convergence time compared with other existing algorithms in both low and high SNRs. The FPNLMS algorithm is further simulated for a real transfer function to show its robustness compared with existing algorithms.

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 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.

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.

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

This paper reports on the development of “full-gradient” and “simplified-gradient” versions of the filtered-u algorithm for active noise control. After discussing the general principles of active noise control, the paper presents full mathematical derivations of the full-gradient, simplified-gradient, and Feintuch-based versions of the filtered-u algorithm, clearly showing the various levels of simplifying assumptions that are made along the way. Finally, some illustrative simulation results are presented.

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.

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.

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.

Modal filtered-x LMS algorithm for global active noise control in a vibro-acoustic cavity