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

Lu, Lu; Yin, Kaili; Lamare, Rodrigo C. de; Zheng, Zongsheng; Yu, Yi; Yang, Xiaobo; Chen, Badong

doi:10.1016/j.sigpro.2020.107929

Cited by 63 publications

(23 citation statements)

References 190 publications

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“…Although the proposed sparsity-aware algorithms in this paper essentially belong to linear adaptive filtering scheme, it also can be extended to the active noise control in linear systems and/or nonlinear systems [30,31] and other fields [32] in the future.…”

Section: Discussionmentioning

confidence: 99%

Robust Normalized Subband Adaptive Filter Algorithm with a Sigmoid-Function-Based Step-Size Scaler and Its Convex Combination Version

Shen

Tang

Yang

2021

Mathematical Problems in Engineering

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In this paper, by inserting the logarithm cost function of the normalized subband adaptive filter algorithm with the step-size scaler (SSS-NSAF) into the sigmoid function structure, the proposed sigmoid-function-based SSS-NSAF algorithm yields improved robustness against impulsive interferences and lower steady-state error. In order to identify sparse impulse response further, a series of sparsity-aware algorithms, including the sigmoid L0 norm constraint SSS-NSAF (SL0-SSS-NSAF), sigmoid step-size scaler improved proportionate NSAF (S-SSS-IPNSAF), and sigmoid L0 norm constraint step-size scaler improved proportionate NSAF (SL0-SSS-IPNSAF), is derived by inserting the logarithm cost function into the sigmoid function structure as well as the L0 norm of the weight coefficient vector to act as a new cost function. Since the use of the fix step size in the proposed SL0-SSS-IPNSAF algorithm, it needs to make a trade-off between fast convergence rate and low steady-state error. Thus, the convex combination version of the SL0-SSS-IPNSAF (CSL0-SSS-IPNSAF) algorithm is proposed. Simulations in acoustic echo cancellation (AEC) scenario have justified the improved performance of these proposed algorithms in impulsive interference environments and even in the impulsive interference-free condition.

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Section: Discussionmentioning

confidence: 99%

Robust Normalized Subband Adaptive Filter Algorithm with a Sigmoid-Function-Based Step-Size Scaler and Its Convex Combination Version

Shen

Tang

Yang

2021

Mathematical Problems in Engineering

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“…Nonlinear filtering techniques have been extensively investigated in the last decade, mainly due to their ability to deal with the inherent nonlinearities of practical systems. Applications of nonlinear adaptive filters, which possess the learning capability to obtain nonlinear system parameters, have been related to system identification [1], echo cancellation [2], noise control [3], and acoustic feedback cancellation in hearing aids [4].…”

Section: Introductionmentioning

confidence: 99%

“…A review of NANC methodologies has been summarized in [47,3] and references therein. However, it is noteworthy that the nonlinear functional expansion often results in expanded inputs with higher-dimensional nonlinearities, and the number of filter weights may become excessive large.…”

Section: Introductionmentioning

confidence: 99%

Study of Frequency domain exponential functional link network filters

Yu¹,

Tana²,

Lamareb³

et al. 2022

Preprint

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The exponential functional link network (EFLN) filter has attracted tremendous interest due to its enhanced nonlinear modeling capability. However, the computational complexity will dramatically increase with the dimension growth of the EFLN-based filter. To improve the computational efficiency, we propose a novel frequency domain exponential functional link network (FDEFLN) filter in this paper. The idea is to organize the samples in blocks of expanded input data, transform them from time domain to frequency domain, and thus execute the filtering and adaptation procedures in frequency domain with the overlap-save method. A FDEFLN-based nonlinear active noise control (NANC) system has also been developed to form the frequency domain exponential filtered-s least mean-square (FDEFsLMS) algorithm. Moreover, the stability, steady-state performance and computational complexity of algorithms are analyzed. Finally, several numerical experiments corroborate the proposed FDEFLN-based algorithms in nonlinear system identification, acoustic echo cancellation and NANC implementations, which demonstrate much better computational efficiency.

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“…In all of these situations, the traditional linear structure cannot model the nonlinear part of the system accurately enough. To overcome these challenges, many researchers have proposed numerous ways when taking the nonlinearity of the system into account, which can be roughly divided into two categories [13,14]. The former one uses artificial neural networks to model the nonlinearity of the system [15,16].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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Nonlinear active control is very important in many practical applications. Many well-known nonlinear active noise control algorithms may suffer from high computational complexity and low convergence speed, especially in the nonlinear secondary path case. Thus, it is still an actively researched topic for reducing complexity and improving the convergence rate. This paper presents a low-complexity Volterra filtered-error least mean square algorithm when taking a decomposable Volterra model into account for active control of nonlinear noise processes, which is referred as DVMFELMS. The computational complexity analysis shows that the proposed DVMFELMS algorithm can significantly reduce the nonlinear active noise control system’s complexity. The simulation results further show that the proposed algorithm can achieve promising performance compared with the Volterra-based FELMS algorithm and other state-of-the-art nonlinear filters, while the decomposable error of the Volterra kernel may be introduced inevitably. Moreover, the proposed DVMFELMS algorithm shows a better convergence rate in the broadband primary noise case due to fewer parameters used in each sub-filter.

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

Cited by 63 publications

References 190 publications

Robust Normalized Subband Adaptive Filter Algorithm with a Sigmoid-Function-Based Step-Size Scaler and Its Convex Combination Version

Robust Normalized Subband Adaptive Filter Algorithm with a Sigmoid-Function-Based Step-Size Scaler and Its Convex Combination Version

Study of Frequency domain exponential functional link network filters

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

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