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

Jabeen, Fakhra; Mirza, Alina; Zeb, Ayesha; Imran, Muhammad; Afzal, Farkhanda; Maqbool, Ayesha

doi:10.1109/access.2021.3105902

Cited by 10 publications

(8 citation statements)

References 31 publications

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“…Experiment 4: Here we use the input reference noise as an impulse noise [36] modeled by a standard symmetric αstable distribution as where parameter α denotes a characteristics exponent, 0 < α < 2. When α is close to 2, we can consider this distribution as a Gaussian function.…”

Section: B Performance Comparison In the Impulsive Noise Environmentmentioning

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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Section: B Performance Comparison In the Impulsive Noise Environmentmentioning

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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“…ANC has been successfully applied in various domains, such as automotive [11], [12] and industrial settings [13], where reducing noise is crucial. However, the effectiveness of ANC systems relies heavily on the accuracy of noise prediction models and the sophistication of the control algorithms used to generate the anti-phase sound waves [14].…”

Section: Introductionmentioning

confidence: 99%

Improving Insertion Loss of Sonic Crystal Active Noise Barrier by Reinforcement Learning and Finite Difference Time Domain Simulations

Ramírez-Solana,

Galiana-Nieves,

Redondo

et al. 2024

IEEE Access

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Sonic crystal noise barriers (SCNB) have emerged as a promising solution for mitigating traffic noise pollution. These barriers utilize periodic structures to selectively reflect acoustic waves at specific target frequencies, offering the advantage of being permeable to light and wind. However, their installation and maintenance costs have hindered widespread adoption. In contrast, active noise control (ANC) systems leverage speakers and microphones to generate opposing sound waves that cancel out incoming noise, presenting a potentially cost-effective alternative. The efficacy of ANC, however, hinges on the precision of noise prediction models and control algorithms.Reinforcement Learning (RL) technique, an interdisciplinary area of machine learning, has shown promise in enhancing ANC systems by enabling them to adapt to changing noise conditions and achieve superior noise reduction, particularly in enclosed spaces. Despite these advancements, several challenges remain in applying RL to ANC systems for SCNB. This paper explores these challenges and proposes an RL-based solution for autonomous ANC systems within the context of SCNB, utilizing a Finite Difference Time Domain (FDTD) simulation environment to address low-frequency, moving sources, and outdoor propagation noise scenarios.

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“…These chambers are designed to eliminate unwanted background noise by covering the internal surfaces with materials that effectively reduce sound from the outside. However, traditional passive noise isolation chambers, which rely on high-density sound-absorbing materials, can be bulky, heavy, and costly [1][2]. Hence, there is a growing demand for a more cost-effective and efficient solution to minimize noise pollution.…”

Section: Introductionmentioning

confidence: 99%

Implementation of active noise cancelling for sound isolation box

Putri,

Hermawanto,

Prajitno

2023

J. Phys.: Conf. Ser.

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A quiet room is essential for acoustical instrument calibration, such as the sound level meter. A small acoustic chamber can be utilized to create an environment that is free from noise. Commonly, it is built with a passive noise-blocking system using high-density absorber material such as rock wool or glass wool. However, this approach results in a room that is both heavy and costly. This paper proposes a mini acoustic chamber that utilizes an active noise cancelling (ANC) system to block the noise. A filtered-x least mean square (FxLMS) algorithm was implemented for the ANC system. A simulation of the system with the recorded actual noise data was conducted to find the optimum filter parameter. The results show that the ANC system could reduce the noise up to 2 dB for certain types of noises. The sound outside the chamber was recorded and processed offline to tune the filter parameter. Evaluation of the effectiveness of the proposed noise reduction system was performed by measuring the sound inside the chamber.

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

Cited by 10 publications

References 31 publications

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

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

Improving Insertion Loss of Sonic Crystal Active Noise Barrier by Reinforcement Learning and Finite Difference Time Domain Simulations

Implementation of active noise cancelling for sound isolation box

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