A Diffusion Strategy for the Multichannel Active Noise Control System in Distributed Network

Song, Ju-man; Park, PooGyeon

doi:10.1109/csci.2016.0130

Cited by 15 publications

(9 citation statements)

References 8 publications

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“…It can achieve a stable behavior over networks regardless of the topology, which is seen as a key advantage. The diffusion FxNLMS (DFxNLMS) algorithm for multi-channel ANC system was developed, whose update equation can be expressed as [361]     …”

Section: Selective Anc Systemsmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Selective Anc Systemsmentioning

confidence: 99%

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

et al. 2021

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“…Lorente et al have proposed a block processing-based distributed algorithm [13] for ANC. Song et al have attempted to incorporate a diffusion strategy in MANC systems and achieve a global solution in [14]. In [15], the multiple channels in ANC systems are considered independent of each other and a decentralised approach has been presented.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…In some prominent works, incremental learning strategy-based ANC [11] and block formulation of distributed ANC controller [13] have been proposed and compared with the centralised approach. Few attempts have been made for developing distributed ANC systems with diffusion scheme of learning [12,14]. In [12], the diffusion of weight vectors obtained from controllers located at different acoustic nodes is not addressed.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…Instead, the individual node uses the information from its neighbouring node and its previous weights in updating its weight vector. In addition, in [14], the comparison of the conventional and proposed method is not supported by analysis of computational burden involved in the algorithm steps. Moreover, these works assume that the sensors and actuators operate in a linear medium and source environment.…”

Section: Introductionmentioning

confidence: 99%

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On distributed non‐linear active noise control using diffusion collaborative learning strategy

Kukde

Panda

Manikandan

2018

IET signal process.

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Active noise control in a non-linear spatial region is a challenging problem, especially for low-frequency noise control applications. The investigation of the existing literature reveals that this problem is tackled by systems with multiple sensors and loudspeakers with a centralised processor. However, the use of centralised techniques is bulky, computationally expensive and lacks scalability. Therefore, the authors propose a distributed learning approach for noise cancellation using a diffusion collaborative strategy. The proposed Legendre-functional link network diffusion filtered s least mean square (FsLMS) algorithm is compared with the standard multi-channel FsLMS algorithm. For different non-linear scenarios, the performance of the proposed method is evaluated in terms of noise reduction performance and computational complexity. It is demonstrated that the proposed method offers significant improvement in noise mitigation performance and computational load as compared with its centralised multi-channel counterpart.

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“…Song proposed a distributed ANC system with the diffusion cooperation mode. Each node cooperates with all its neighbors, which increases the traffic requirements of the network [12]. A mixed‐error approach combining all the error signals into one signal was proposed by Murao, and this algorithm had the merit of very low computational complexity [13].…”

Section: Introductionmentioning

confidence: 99%

A Subband Adaptive Filtering for Distributed Active Noise Control Systems

Lei

Chen

2021

IEEJ Transactions Elec Engng

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This study proposes an extension of the subband adaptive filtering technique to distributed active noise control systems and the details of the algorithm are presented. The computational requirements and convergence performance are examined for a network with a ring topology without constraints in communication. The proposed algorithm divides the signal of each node into several subbands to reduce the dynamic range of the spectrum and consequently accelerate the convergence speed. Since the subband decomposition contains a decimation process, the computational complexity is reduced as the number of subbands is increased. Simulations are conducted to compare different algorithms and an 8‐node network is studied in terms of noise reduction performance. The results demonstrate the efficacy of the proposed algorithm for broadband active noise control and the convergence speed could be improved compared to the distributed multiple error filtered‐x (DMEFxLMS) algorithm. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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A Diffusion Strategy for the Multichannel Active Noise Control System in Distributed Network

Cited by 15 publications

References 8 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

On distributed non‐linear active noise control using diffusion collaborative learning strategy

A Subband Adaptive Filtering for Distributed Active Noise Control Systems

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