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|Title:||Reduced complexity diffusion filtered x least mean square algorithm for distributed active noise cancellation|
|Keywords:||Active noise control (ANC)|
Centralized multi-channel ANC
Diffusion cooperation learning
Distributed noise cancellation
Filtered x least mean square algorithm
Secondary path effects
|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.|
|Appears in Collections:||Research Publications|
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