A novel FELMS-based narrowband active noise control system and its convergence analysis

Zhu, Wenzhao; Luo, Lei; Xie, Antai; Sun, Jinwei

doi:10.1016/j.apacoust.2019.07.003

Cited by 22 publications

(9 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The existing works of NANC are outlined in Table 6. [211](Also for For uncorrelated narrowband disturbances [201](Also for [22,23,224] band-limited multi-tonal [223] For band-limited white noises and tonal signals white noise) signals) [21,167,168] 2.3. Filtered-u ANC family Several fundamental filtered-u ANC algorithms have been proposed before the past decade.…”

Section: ) Felms-based Algorithms For Narrowband Noisementioning

confidence: 99%

See 1 more Smart Citation

A survey on active noise control in the past decade—Part I: Linear systems

et al. 2021

View full text Add to dashboard Cite

Active noise control (ANC) is an effective way for reducing the noise level in electroacoustic or electromechanical systems. Since its first introduction in 1936, this approach has been greatly developed. This paper focuses on discussing the development of ANC techniques over the past decade. Linear ANC algorithms, including the celebrated filtered-x least-mean-square (FxLMS)based algorithms and distributed ANC algorithms, are investigated and evaluated. Nonlinear ANC (NLANC) techniques, such as functional link artificial neural network (FLANN)-based algorithms, are pursued in Part II. Furthermore, some novel methods and applications of ANC emerging in the past decade are summarized. Finally, future research challenges regarding the ANC technique are discussed.

show abstract

Section: ) Felms-based Algorithms For Narrowband Noisementioning

confidence: 99%

“…Therefore, it is natural to consider using FeLMS-based algorithms for NANC. In [223], the FeLMS algorithm was intentionally introduced to deal with the noise in NANC systems. Moreover, the convergence behavior of the algorithm was analyzed.…”

Section: ) Felms-based Algorithms For Narrowband Noisementioning

confidence: 99%

A survey on active noise control in the past decade—Part I: Linear systems

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Unlike the MSE rule, HOEP uses high-order errors to define cost functions, such as E[e 3 (k)], E[e 4 (k)], and so on. Since the Taylor expansion of exp(e 2 (k)) can be expressed as 1 + 1 1! e 2 (k) + 1 2!…”

Section: A Review Of MCCmentioning

confidence: 99%

“…Adaptive filtering technique has been successfully applied in diverse signal processing fields, such as active noise control, system identification, and adaptive echo cancellation [1]- [3]. The most popular algorithm used in such a technique is the least-mean-square (LMS) algorithm because of its simplicity [4].…”

Section: Introductionmentioning

confidence: 99%

Affine Projection Algorithm by Employing Maximum Correntropy Criterion for System Identification of Mixed Noise

Wang

Han

2019

IEEE Access

View full text Add to dashboard Cite

The adaptive algorithms have been widely studied in Gaussian environment. However, the impulsive noise and other non-Gaussian noise may largely deteriorate the performance of algorithm in practical applications. To address this problem, in this paper, we propose two novel adaptive algorithms for system identification problem with mixed noise scenarios. Both proposed algorithms are based on the framework of the affine projection (AP) algorithm. The first proposed algorithm, termed as VS-APMCCA, combines variable step-size (VS) strategy and maximum correntropy criterion (MCC) to obtain improved performance. For further performance improvement, the VC-VS-APMCCA is developed, which is based on the variable center (VC) scheme of MCC. The convergence analysis of the VC-VS-APMCCA is conduced. Finally, simulation results demonstrate the superior performance of the VS-APMCCA and VC-VS-APMCCA. INDEX TERMS Affine projection algorithm, variable step-size, maximum correntropy criterion (MCC), variable center (VC), mixed noise.

show abstract

“…The narrowband active noise control (NANC) system equipped with the narrowband filteredx LMS (NFxLMS) algorithm is found to be effective in reducing such noises [2,3]. The stability analysis of the NFxLMS, which is mainly aimed at estimating the upper bound for the step size, that is μ max , can be performed in the time domain [4][5][6] or in the Z-domain [7][8][9][10][11]. However, both analyses mentioned above have their respective limitations.…”

Section: Introductionmentioning

confidence: 99%

An analysis of the convergence condition for narrowband FxLMS

Wen

Luo

Yang

2021

Electronics Letters

View full text Add to dashboard Cite

This letter discusses the convergence condition of the narrowband FxLMS (NFxLMS) algorithm in Z‐domain. First, two new concepts about NFxLMS root locus, that is critical start point (CSP) and critical cross point (CCP), are introduced and defined. For NFxLMS with the simplest case, a special characteristic of the root locus is observed that the CCP always locates itself near the CSP in the Z plane. By exploiting this characteristic, directly solving the root locus equation is eliminated from the stability analysis, and the upper bound of the step size for NFxLMS is eventually estimated as a function of the reference frequency.

show abstract

A novel FELMS-based narrowband active noise control system and its convergence analysis

Cited by 22 publications

References 17 publications

A survey on active noise control in the past decade—Part I: Linear systems

A survey on active noise control in the past decade—Part I: Linear systems

Affine Projection Algorithm by Employing Maximum Correntropy Criterion for System Identification of Mixed Noise

An analysis of the convergence condition for narrowband FxLMS

Contact Info

Product

Resources

About