Mixed-Gradients Distributed Filtered Reference Least Mean Square Algorithm - A Robust Distributed Multichannel Active Noise Control Algorithm

Distributed multichannel active noise control (DMCANC), which utilizes multiple individual processors to achieve a global noise reduction performance comparable to conventional centralized multichannel active noise control (MCANC), has become increasingly attractive due to its high computational eff...

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Vydáno v:IEEE Transactions on Audio, Speech and Language Processing Ročník 33; s. 1563 - 1575
Hlavní autoři: Ji, Junwei, Shi, Dongyuan, Gan, Woon-Seng
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 2025
Témata:
Acoustics
Active Noise Control (ANC)
auto -shrink step size (ASSS)
communication restrictions
compensation filter
Crosstalk
Delays
distributed control
Filtering algorithms
Information filters
mixed -gradient distributed filtered reference least mean square (MGDFxLMS)
Noise
Noise reduction
Sensors
Signal processing algorithms
Training
ISSN:2998-4173, 2998-4173
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Shrnutí:Distributed multichannel active noise control (DMCANC), which utilizes multiple individual processors to achieve a global noise reduction performance comparable to conventional centralized multichannel active noise control (MCANC), has become increasingly attractive due to its high computational efficiency. However, the majority of current DMCANC algorithms disregard the impact of crosstalk across nodes and impose the assumption of an ideal network devoid of communication limitations, which is an unrealistic assumption. Therefore, this work presents a robust DMCANC algorithm that employs the compensating filter to mitigate the impact of crosstalk. The proposed solution enhances the DMCANC system's flexibility and security by utilizing local gradients instead of local control filters to convey enhanced information, resulting in a mixed-gradients distributed filtered reference least mean square (MGDFxLMS) algorithm. The performance investigation demonstrates that the proposed approach performs well with the centralized method. Furthermore, to address the issue of communication delay in the distributed network, a practical strategy that auto-shrinks the step size value in response to the delayed samples is implemented to improve the system's resilience. The numerical simulation results demonstrate the efficacy of the proposed auto-shrink step size MGDFxLMS (ASSS-MGDFxLMS) algorithm across various communication delays, highlighting its practical value.
ISSN:2998-4173
2998-4173
DOI:10.1109/TASLPRO.2025.3552932