access icon free On distributed non-linear active noise control using diffusion collaborative learning strategy

© The Institution of Engineering and Technology
Received 19/07/2017, Accepted 20/11/2017, Revised 16/11/2017, Published 21/11/2017

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.

Inspec keywords: loudspeakers; least mean squares methods; active noise control; computational complexity

Other keywords: computational complexity; multiple sensors; loudspeakers; nonlinear spatial region; least mean square algorithm; centralised multichannel counterpart; distributed nonlinear active noise control; noise cancellation; Legendre-functional link network diffusion; standard multichannel FsLMS algorithm; distributed learning approach; low-frequency noise control; centralised processor; diffusion collaborative learning strategy; noise reduction performance

Subjects: Interpolation and function approximation (numerical analysis); Signal processing theory; Hi-Fi equipment and systems; Interpolation and function approximation (numerical analysis); Filtering methods in signal processing

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