A recursive least square algorithm for active control of mixed noise

Wu, L, Qiu, X, Burnett, I and Guo, Y 2014, 'A recursive least square algorithm for active control of mixed noise', Journal of Sound and Vibration, vol. 339, pp. 1-10.


Document type: Journal Article
Collection: Journal Articles

Title A recursive least square algorithm for active control of mixed noise
Author(s) Wu, L
Qiu, X
Burnett, I
Guo, Y
Year 2014
Journal name Journal of Sound and Vibration
Volume number 339
Start page 1
End page 10
Total pages 10
Publisher Elsevier Ltd
Abstract Most currently available active control algorithms target noise sources with relatively singular characteristics such as tonal or wideband noise. However, noise in some practical environments is a mixture of different sounds generated by different sources, where the existing algorithms designed for single noise sources may not be optimal. This kind of noise is generally referred to as mixed noise, and this paper develops a specific active control algorithm for mixed noise based on the recursive least square structure. By minimizing the weighted summation of the logarithmic transformation of posterior errors and taking the commutation error into consideration, the proposed algorithm not only reduces broadband, narrowband and impulse noise successfully, but also mixtures of them. Simulation results demonstrate the superiority of the proposed algorithm over existing algorithms such as the filtered-x least mean square, filtered-x logarithmic least mean square, filtered-x normalized least mean square and filtered weight filtered-x normalized least mean square algorithms in terms of convergence rate and noise reduction.
Subject Physical Sciences not elsewhere classified
Engineering not elsewhere classified
Keyword(s) Adaptive algorithms
Impulse noise
Mixtures
Noise abatement Filtered x least mean squares
Logarithmic transformations
Normalized least mean square
Normalized least mean square algorithms
Recursive least square (RLS)
Recursive Least Square algorithm
Singular characteristics
Weighted summations
DOI - identifier 10.1016/j.jsv.2014.11.002
Copyright notice © 2014 Elsevier Ltd.
ISSN 0022-460X
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