A Low-Complexity Volterra Filtered-Error LMS Algorithm with a Kronecker Product Decomposition

Zhang, Jinhui; Zheng, Chengshi; Zhang, Fang‐Jie; Li, Xiaodong

doi:10.3390/app11209637

Cited by 4 publications

(3 citation statements)

References 40 publications

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“…In the case with two reference microphones, two secondary sound sources, and two error microphones, the increased multiplication operations of Bai's method and the proposed method are about 4% and 9%. In practical applications, virtual microphone technology [34,35] is often used in low-sampling-rate regions, so the proportion of computational complexity introduced by the proposed method will be further reduced.…”

Section: Analysis Of Computational Complexitymentioning

confidence: 99%

Dynamic Matching of Reconstruction and Anti-Aliasing Filters in Adaptive Active Noise Control

Zhang,

Wu,

Wang

et al. 2024

Applied Sciences

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Constrained by the computing power, adaptive active noise control systems often have a low sampling rate. Therefore, reconstruction filters and anti-aliasing filters with fixed parameters are generally adopted to eliminate the mirror noise and aliasing noise, respectively; however, they may boost the group delay of the system. A dynamic matching method based on dual sampling rates is proposed to dynamically adjust the parameters of the reconstruction and anti-aliasing filters, according to the characteristics of the primary sound source, for a compromise between high-frequency noise and group delay. In digital high-sampling-rate regions, data that include high-frequency information are analyzed regularly, following which the parameters of the reconstruction filters and those of the anti-aliasing filters are dynamically matched. In digital low-sampling-rate regions, the estimation of the secondary path transfer function is updated. The results of laboratory experiments show that the proposed method not only can suppress the mirror and aliasing noise for primary sound sources with different spectra, but can also effectively reduce the group delay and improve the noise reduction performance of a system.

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Section: Analysis Of Computational Complexitymentioning

confidence: 99%

Dynamic Matching of Reconstruction and Anti-Aliasing Filters in Adaptive Active Noise Control

Zhang,

Wu,

Wang

et al. 2024

Applied Sciences

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“…The effect on medium-and high-frequency noise is better than that on low-frequency noise, which remains prone to diffraction due to its long wavelength, limiting the noise reduction effect. Active noise control technology is often used to degrade low-frequency noise [4,5].…”

Section: Introductionmentioning

confidence: 99%

A Simplified Frequency-Domain Feedback Active Noise Control Algorithm

Gao,

Yu,

Gao

2024

Applied Sciences

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When the adaptive filter length is increased, the calculation complexity increases rapidly because the relationship between the calculation and the adaptive filter length N contains a power function with no secondary path identification algorithm. Under the basic premise of unreduced noise reduction, herein, a simplified frequency-domain feedback active noise control algorithm is proposed. To reduce the computation complexity, the total delay is adopted as the estimated secondary path; the filtered reference signal is produced in the frequency domain by using multiplication to replace convolution calculation in the time domain and then updating the adaptive filter coefficients in the frequency domain. Therefore, the computational complexity has a logarithmic function with the increased adaptive filter length in the proposed algorithm. If the adaptive filter length is 512, the existing WSMANC algorithm’s calculation is 271,360 real number multiplications, while that of the proposed algorithm is only 38,912 real number multiplications. To verify the proposed algorithm’s stability, convergence speed, and noise reduction, the single-frequency noise, narrowband white noise, and narrowband pink noise, respectively, are used as the primary noise types in the simulations. The results show that (1) the proposed SFDFBANC algorithm can obtain similar noise reduction performance to existing algorithm, (2) the convergence rate is faster than existing algorithm, and (3) if the adaptive filter length is more than 64, the proposed algorithm exhibits a lower computational complexity.

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“…In nonlinear signal processing applications, such as echo cancelation, 1 nonlinear system identification, 2–5 nonlinear active noise control 6,7 and so on, a successful model widely used to process signals is the Volterra series of a dynamic system, which can be regarded as a Taylor expansion with memory 8 . However, the computational complexity of Volterra extremely limits its development as the filter order (or memory size) increasing 9–14 . To address this problem, 15 proposes the interpolated filter to set some of each order parameters zeros to reduce the computational cost under the filter performance without influence.…”

Section: Introductionmentioning

confidence: 99%

A robust adaptive decomposable Volterra filter based on the hyperbolic tangent Leclerc function and its performance analysis

Liu,

Li,

2024

Adaptive Control & Signal

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SummaryMost of the existing adaptive filter algorithms pay more attention to improving performance while ignoring the computational complexity and the impact of the impulsive environment. When encountering the impulsive noise environments, the performance of traditional nonlinear adaptive filter may be significantly reduced and usually needs high computational cost. Therefore, this article proposes a hyperbolic tangent Leclerc robust nonlinear adaptive filter based on the low complexity decomposable Volterra model (HTLNAF‐DVM). The filter is implemented by imposing a rank‐one structure on the full Volterra model to get a product of linear filters, and employs a hyperbolic tangent Leclerc function as a robust norm to effectively improve the robustness against the impulsive noise. In addition, we give the theoretical analyses of the steady‐state mean‐square performance of the proposed HTLNAF‐DVM. Finally, the simulation results prove that the proposed HTLNAF‐DVM algorithm has better performance than the existing algorithms and fit well with the theory.

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A Low-Complexity Volterra Filtered-Error LMS Algorithm with a Kronecker Product Decomposition

Cited by 4 publications

References 40 publications

Dynamic Matching of Reconstruction and Anti-Aliasing Filters in Adaptive Active Noise Control

Dynamic Matching of Reconstruction and Anti-Aliasing Filters in Adaptive Active Noise Control

A Simplified Frequency-Domain Feedback Active Noise Control Algorithm

A robust adaptive decomposable Volterra filter based on the hyperbolic tangent Leclerc function and its performance analysis

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