A novel set membership fast NLMS algorithm for acoustic echo cancellation

Hassani, Islam; Arezki, Madjid; Benallal, Ahmed

doi:10.1016/j.apacoust.2020.107210

Cited by 20 publications

(6 citation statements)

References 24 publications

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“…$ 89: (11) Específicamente, si el conjunto 𝜓(𝑛) se encuentra en ℋ(𝑖), no se requiere calcular los coeficientes del vector 𝐰, ya que estos se encuentran dentro del conjunto solución.…”

Section: 𝜓(𝑛) = ⋂ ℋ(𝑖)unclassified

“…En años recientes, diversos autores han propuesto métodos para reducir el costo computacional de los algoritmos adaptativos. Una de las técnicas más eficientes es la de conjunto de membresías (SM -Set-Membership) [10][11][12], en la cual los algoritmos actualizan los coeficientes del filtro si la señal de error es mayor a un umbral previamente establecido, por lo tanto, el número de operaciones se disminuye considerablemente una vez que la potencia del error se ha reducido.…”

Section: Introductionunclassified

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Nueva variante del algoritmo NLMS/F de bajo costo computacional

Hernández

Piña

Rosales

et al. 2022

Elek.

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El filtrado adaptativo es utilizado ampliamente en aplicaciones de procesamiento de señales, entre las que se encuentran: cancelación de eco acústico, identificación de sistemas, ecualización de canales, entre otras. El elemento más importante de un filtro adaptativo es el algoritmo adaptativo, el cual tiene la función de ajustar los coeficientes del filtro para minimizar la señal de error. Por tal motivo, es necesario un algoritmo adaptativo que presente una baja carga computacional y una alta velocidad de convergencia. En este artículo, se presenta una nueva variante del algoritmo de mínimos promediados de cuarto orden normalizado (NLMF - Normalized Least-Mean-Fourth) basado en el conjunto de membresías, además, se propone un método que permite ajustar el factor de convergencia de forma automática. Para evaluar su funcionamiento, el algoritmo se simuló en un identificador de sistemas y un cancelador de eco acústico. Los resultados obtenidos demuestran que el algoritmo propuesto mejora la velocidad de convergencia, además de exhibir un bajo costo computacional en comparación con el algoritmo NLMS/F convencional.

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Section: 𝜓(𝑛) = ⋂ ℋ(𝑖)unclassified

Section: Introductionunclassified

Nueva variante del algoritmo NLMS/F de bajo costo computacional

Hernández

Piña

Rosales

et al. 2022

Elek.

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“…In work [18], a nonlinear adaptive filter based on the kernel set membership method was introduced for the purpose of reducing complexity and increasing tracking ability. Many improvements to the set membership NLMS algorithm for the acoustic echo and sparse channel estimation problem have been proposed [19]- [22]. To reduce power consumption in wireless sensor networks, solutions based on SM filtering strategy have also received the attention of many researchers [23], [24].…”

Section: Introductionmentioning

confidence: 99%

Filtered-x Set Membership Algorithm With Time-Varying Error Bound for Nonlinear Active Noise Control

Viet

2022

IEEE Access

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In order to reduce the computational complexity for nonlinear Active Noise Control (ANC) systems, we propose a filter-x set membership with a time-varying error bound (Fx-SM-VEB) algorithm based on the adaptive approximation principle and data-selective update strategy. By introducing the timevarying error bound (VEB) to replace a pre-specified threshold when nonlinearity exists in the components of the ANC system, the performance of the proposed algorithm is significantly improved. Moreover, the VEB is also easily expanded against the impulsive noise that is normally encountered in actual ANC systems. Based on the impulsive-free estimation, we develop a robust Fx-SM-VEB (RFx-SM-VEB) algorithm for the ANC system which is corrupted by impulsive noise in the reference input signal. Besides, we provide analyses of the steady-state behavior, stability conditions, and computational complexity of the proposed algorithms. Many simulation results in different scenarios of the ANC system have shown that the proposed algorithms are efficient under the nonlinear environment and impulsive noise.INDEX TERMS Nonlinear active noise control, Set membership, Filtered-x Least mean square (Fx-LMS) algorithm, impulsive noise.

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“…NLMS provides faster adaptive calculation and ensures an increasingly stable combination based on various input signal powers [31]. However, although many scholars have improved the LMS algorithm [32][33][34], for wideband signals, the noise reduction effect of using only the LMS algorithm is uneven, and the performance is unstable.…”

Section: Introductionmentioning

confidence: 99%

A Denoising Method of Micro-Turbine Acoustic Pressure Signal Based on CEEMDAN and Improved Variable Step-Size NLMS Algorithm

Chen

Zhai

et al. 2022

Machines

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The acoustic pressure signal generated by blades is one of the key indicators for condition monitoring and fault diagnosis in the field of turbines. Generally, the working conditions of the turbine are harsh, resulting in a large amount of interference and noise in the measured acoustic pressure signal. Therefore, denoising the acoustic pressure signal is the basis of the subsequent research. In this paper, a denoising method of micro-turbine acoustic pressure signal based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variable step-size Normalized Least Mean Square (VSS-NLMS) algorithms is proposed. Firstly, the CEEMDAN algorithm is used to decompose the original signal into multiple intrinsic mode functions (IMFs), based on the cross-correlation coefficient and continuous mean square error (CMSE) criterion; the obtained IMFs are divided into clear IMFs, noise-dominated IMFs, and noise IMFs. Finally, the improved VSS-NLMS algorithm is adopted to denoise the noise-dominated IMFs and combined with the clear IMF for reconstruction to obtain the final denoised signal. Adopting the above principles, the acoustic pressure signals generated by a micro-turbine with different rotation speeds and different states (normal turbine and fractured turbine) are denoised, respectively, and the results are compared with the axial flow fan test (ideal interference-free signal). The results show that the denoising method proposed in this paper has a good denoising effect, and the denoised signal is smooth and the important features are well preserved, which is conducive to the extraction of acoustic pressure signal characteristics.

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A novel set membership fast NLMS algorithm for acoustic echo cancellation

Cited by 20 publications

References 24 publications

Nueva variante del algoritmo NLMS/F de bajo costo computacional

Nueva variante del algoritmo NLMS/F de bajo costo computacional

Filtered-x Set Membership Algorithm With Time-Varying Error Bound for Nonlinear Active Noise Control

A Denoising Method of Micro-Turbine Acoustic Pressure Signal Based on CEEMDAN and Improved Variable Step-Size NLMS Algorithm

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