A switching strategy of the frequency-domain adaptive algorithm for active noise control

Wang, J; Xue, J; Lu, J; Qiu, X

A switching strategy of the frequency-domain adaptive algorithm for active noise control

Wang, J Xue, J Lu, J Qiu, X

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Publication Type:: Journal Article
Citation:: Journal of the Acoustical Society of America, 2019, 146 (2), pp. 1045 - 1050
Issue Date:: 2019-08-01

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Field	Value	Language
dc.contributor.author	Wang, J	en_US
dc.contributor.author	Xue, J	en_US
dc.contributor.author	Lu, J	en_US
dc.contributor.author	Qiu, X https://orcid.org/0000-0002-5181-1220	en_US
dc.date.available	2020-05-25T19:06:35Z
dc.date.issued	2019-08-01	en_US
dc.identifier.citation	Journal of the Acoustical Society of America, 2019, 146 (2), pp. 1045 - 1050	en_US
dc.identifier.issn	0001-4966	en_US
dc.identifier.uri	http://hdl.handle.net/10453/136293
dc.description.abstract	© 2019 Acoustical Society of America. The bin-normalized frequency domain block least mean square (NFBLMS) algorithm is a good choice in the active noise control system due to its benefit of high convergence speed. However, it suffers from a biased steady-state solution with insufficient adaptive filter length, often unavoidable due to the influence of the secondary path. A modified frequency domain block least mean square (MFBLMS) algorithm with guaranteed optimal steady-state performance has been proposed recently. However, its convergence speed is generally lower than that of the NFBLMS algorithm. In this paper, a mixed algorithm combining the NFBLMS and MFBLMS algorithms is proposed based on the analysis of the initial convergence trajectory of the NFBLMS algorithm. An effective switching strategy is designed, enabling the MFBLMS algorithm after the NFBLMS algorithm approaches its steady state and shifting back to the NFBLMS algorithm when the change of environment is detected. The mixed algorithm has both the high convergence speed and the optimal steady-state performance, and its effectiveness is validated by simulations using measured acoustic transfer functions.	en_US
dc.relation	http://purl.org/au-research/grants/arc/ARC LP140100987
dc.relation.ispartof	Journal of the Acoustical Society of America	en_US
dc.relation.isbasedon	10.1121/1.5120260	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Acoustics	en_US
dc.title	A switching strategy of the frequency-domain adaptive algorithm for active noise control	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	146	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	open_access	*
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	146	en_US

Abstract:

© 2019 Acoustical Society of America. The bin-normalized frequency domain block least mean square (NFBLMS) algorithm is a good choice in the active noise control system due to its benefit of high convergence speed. However, it suffers from a biased steady-state solution with insufficient adaptive filter length, often unavoidable due to the influence of the secondary path. A modified frequency domain block least mean square (MFBLMS) algorithm with guaranteed optimal steady-state performance has been proposed recently. However, its convergence speed is generally lower than that of the NFBLMS algorithm. In this paper, a mixed algorithm combining the NFBLMS and MFBLMS algorithms is proposed based on the analysis of the initial convergence trajectory of the NFBLMS algorithm. An effective switching strategy is designed, enabling the MFBLMS algorithm after the NFBLMS algorithm approaches its steady state and shifting back to the NFBLMS algorithm when the change of environment is detected. The mixed algorithm has both the high convergence speed and the optimal steady-state performance, and its effectiveness is validated by simulations using measured acoustic transfer functions.

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http://hdl.handle.net/10453/136293