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Please use this identifier to cite or link to this item: http://idr.iitbbs.ac.in/jspui/handle/2008/2261
Title: Reduced complexity diffusion filtered x least mean square algorithm for distributed active noise cancellation
Authors: Kukde R.
Manikandan M.S.
Panda G.
Keywords: Active noise control (ANC)
Centralized multi-channel ANC
Diffusion cooperation learning
Distributed noise cancellation
Filtered x least mean square algorithm
Secondary path effects
Issue Date: 2019
Abstract: A computationally efficient diffusion cooperation scheme-based distributed active noise control (DANC) system is proposed in this paper. It is observed that the conventional centralized multi-channel ANC (MANC) systems employed for noise reduction in a wide region are computationally complex and lack scalability. Additionally, the noise reduction for practically encountered noises is a challenging task, especially for multi-point environments. To overcome these drawbacks, in this paper, a diffusion filtered x least mean square (DFxLMS) algorithm is developed for DANC systems. The proposed DFxLMS-DANC scheme is modified using proximal secondary path bounds to reduce computational overhead. Also, the practical application of air-conditioner noise control is addressed in the presence of real primary and secondary path scenarios. It is shown that the total computational improvement in proposed DFxLMS-DANC and modified DFxLMS-DANC systems is 23.13% and 49.87%, respectively, over multiple error FxLMS-based MANC system. It is also demonstrated that the proposed method helps to achieve ? 18�dB reduction in the air-conditioner noise levels in practical environments. � 2019, Springer-Verlag London Ltd., part of Springer Nature.
URI: http://dx.doi.org/10.1007/s11760-018-01412-1
http://10.10.32.48:8080/jspui/handle/2008/2261
Appears in Collections:Research Publications

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