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Reduced complexity diffusion filtered x least mean square algorithm for distributed active noise cancellation

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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 \(\sim \) 18 dB reduction in the air-conditioner noise levels in practical environments.

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Author notes

  1. Ganapati Panda

    Present address: C. V. Raman College of Engineering, Bhubaneswar, Odisha, India

Authors and Affiliations

  1. School of Electrical Sciences, Indian Institute of Technology Bhubaneswar, Bhubaneswar, Odisha, India

    Ruchi Kukde, M. Sabarimalai Manikandan & Ganapati Panda

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  1. Ruchi Kukde
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  2. M. Sabarimalai Manikandan
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  3. Ganapati Panda
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Correspondence to Ruchi Kukde.

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This work was carried out as a part of Ph.D. thesis work at the School of Electrical Sciences, Indian Institute of Technology Bhubaneswar, Odisha, India.

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Kukde, R., Manikandan, M.S. & Panda, G. Reduced complexity diffusion filtered x least mean square algorithm for distributed active noise cancellation. SIViP 13, 447–455 (2019). https://doi.org/10.1007/s11760-018-01412-1

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  • DOI: https://doi.org/10.1007/s11760-018-01412-1

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