Improving performance of FxRLS algorithm for active noise control of impulsive noise

This paper proposes a new active noise control (ANC) system based on a recursive least-squares lattice (RLSL) algorithm by designing secondary-path innovation (SPI) and lattice-order decision (LOD) algorithms. The SPI algorithm associated with the power spectral factorization of the secondary path is designed to apply the RLSL algorithm to the ANC system without the filtered-input structure. The SPI algorithm whitens the error microphone signals into the virtual error signals just ahead of the secondary path to construct the virtual desired signals corresponding to the outputs of the lattice filter. The LOD algorithm is designed to operate the ANC system without knowing the tap length of the optimal finite impulse response (FIR) filter. The decision algorithm using the initial and final estimation errors of the lattice filter estimates the tap length of the optimal FIR filter by increasing the lattice order one-by-one until the final estimation error reaches below a threshold value. Even in environments where the tap length of the optimal FIR filter is unknown, the proposed ANC system with low computational complexity shows almost the same performance as the ANC system based on a filtered-input recursive least squares algorithm.

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“…In the equation (19), the inverse of the first spectral factorization form is defined for the innovation filter K (z) as…”

Section: A Secondary-path Innovation Algorithmmentioning

confidence: 99%

Recursive Least-Squares Lattice Algorithm Combined With Secondary-Path Innovation and Lattice-Order Decision Algorithms for Active Noise Control

Kim

Park

2020

IEEE Access

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“…Many novel variants of the FxRLS algorithm have also been proposed in the literature with improved performances [27]- [32], however, none of them combines online secondary path modeling with the active noise control for impulsive input. Moreover, using [26], we have incorporated the characteristic of robustness into our proposed methods too.…”

Section: Introductionmentioning

confidence: 99%

“…This method attained faster convergence than the reported variants of the FxLMS algorithm. However, the FxRLS algorithm is not much robust for non-stationary acoustic paths, and stability is not assured under high IN [2], [26]. To cater to this problem, a second proposed MGFxRLS-FxRLS method that employs the MGFxRLS algorithm [26] in ANC filter and FxRLS algorithm in OSPM filter is devised.…”

Section: Introductionmentioning

confidence: 99%

“…However, the FxRLS algorithm is not much robust for non-stationary acoustic paths, and stability is not assured under high IN [2], [26]. To cater to this problem, a second proposed MGFxRLS-FxRLS method that employs the MGFxRLS algorithm [26] in ANC filter and FxRLS algorithm in OSPM filter is devised. This further improved the robustness of the overall system.…”

Section: Introductionmentioning

confidence: 99%

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FxRLS Algorithms Based Active Control of Impulsive Noise With Online Secondary Path Modeling

et al. 2021

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Active noise control algorithms undergo stability problems in the presence of impulsive noise. This paper investigates such algorithms with online secondary path modeling for impulsive noise and varying acoustic paths. The paper presents three methods for active noise control, along with improved online secondary path modeling. Firstly, the filtered x recursive least square algorithm is applied for both active noise control and online secondary path modeling. This method gave faster convergence, improved stability, and modeling accuracy as compared to existing ones. The filtered x recursive least square algorithm is not robust for abruptly changing acoustic paths. To overcome this problem another method that uses modified gain filtered x recursive least square algorithm for active noise control is presented. Furthermore, it is observed that modified gain filtered x recursive least square achieves the desired performance with overheads of increased complexity. Thus, a hybrid method is proposed which has less computational complexity than the rest methods with no compromise on active noise control system performance.

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“…Similar to the LMS-based algorithms [21,22], the VFxMCC algorithm uses the gradient descent theory to update the weight vector. Moreover, the weight coefficient updating mode of VFxRMC algorithm is parallel to the recursive least square (RLS) based algorithms [23][24][25]. In order to further improve algorithm performance, we use the VFxRMC algorithm to update the 1st-order SOV filter coefficient and the 2nd-order SOV filter coefficient is updated by VFxMCC algorithm, which is called the hybrid (HVFx-RMC-MCC) algorithm.…”

Section: Introductionmentioning

confidence: 99%

An efficient algorithm for nonlinear active noise control of impulsive noise

2019

Applied Acoustics

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Nonlinear active noise control (NANC) systems employing Volterra filter suffer from the stability issues in the presence of impulsive noise. To solve this problem, we combine the second-order Volterra (SOV) filter and maximum correntropy criterion (MCC) in this paper. The Volterra filter-x maximum correntropy criterion (VFxMCC) algorithm and Volterra filter-x recursive maximum correntropy (VFxRMC) algorithm are applied to reduce the impulsive noise of NANC. We find that VFxMCC algorithm has a low computational complexity and VFxRMC algorithm converges fast. In order to extract their advantages, we further propose a hybrid algorithm based on the VFxMCC and VFxRMC algorithms. In addition, the normalize step-size version of VFxMCC (VFxnMCC) algorithm is developed to improve the robustness and performance. Meanwhile, we adaptively adjust the kernel size of MCC online based on the sample variance of reference signal to improve the performance of the proposed algorithms. Simulation results in the context of nonlinear active impulsive noise control demonstrate that the proposed algorithms achieve much better performance than the existing algorithms in various noise environments.

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Improving performance of FxRLS algorithm for active noise control of impulsive noise

Cited by 36 publications

References 15 publications

Recursive Least-Squares Lattice Algorithm Combined With Secondary-Path Innovation and Lattice-Order Decision Algorithms for Active Noise Control

Recursive Least-Squares Lattice Algorithm Combined With Secondary-Path Innovation and Lattice-Order Decision Algorithms for Active Noise Control

FxRLS Algorithms Based Active Control of Impulsive Noise With Online Secondary Path Modeling

An efficient algorithm for nonlinear active noise control of impulsive noise

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