Robust acoustic contrast control with reduced in-situ measurement by acoustic modeling

Zhu, Q; Coleman, P; Wu, M; Yang, J

Robust acoustic contrast control with reduced in-situ measurement by acoustic modeling

Zhu, Q

Coleman, P Wu, M Yang, J

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Publisher:: Audio Engineering Society
Publication Type:: Journal Article
Citation:: Journal of the Audio Engineering Society, 2017, 65, (6), pp. 460-473
Issue Date:: 2017-06-01

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Field	Value	Language
dc.contributor.author	Zhu, Q https://orcid.org/0000-0003-3942-0945
dc.contributor.author	Coleman, P
dc.contributor.author	Wu, M
dc.contributor.author	Yang, J
dc.date.accessioned	2022-08-05T03:24:50Z
dc.date.available	2022-08-05T03:24:50Z
dc.date.issued	2017-06-01
dc.identifier.citation	Journal of the Audio Engineering Society, 2017, 65, (6), pp. 460-473
dc.identifier.issn	0004-7554
dc.identifier.issn	1549-4950
dc.identifier.uri	http://hdl.handle.net/10453/159642
dc.description.abstract	Personal audio systems generate a local sound field for a listener while attenuating the sound energy at pre-defined quiet zones. In practice, system performance is sensitive to errors in the acoustic transfer functions between the sources and the zones. Regularization is commonly used to improve robustness, however, selecting a regularization parameter is not always straightforward. In this paper a design framework for robust reproduction is proposed, combining transfer function and error modeling. The framework allows a physical perspective on the regularization required for a system, based on the bound of assumed additive or multiplicative errors, which is obtained by acoustic modeling. Acoustic contrast control is separately combined with worst-case and probability-model optimization, exploiting limited knowledge of the potential error distribution. Monte-Carlo simulations show that these approaches give increased system robustness compared to the state of the art approaches for regularization parameter estimation, and experimental results verify that robust sound zone control is achieved in the presence of loudspeaker gain errors. Furthermore, by applying the proposed framework, in-situ transfer function measurements were reduced to a single measurement per loudspeaker, per zone, with limited acoustic contrast degradation of less than 2 dB over 100-3000 Hz compared to the fully measured regularized case.
dc.language	English
dc.publisher	Audio Engineering Society
dc.relation.ispartof	Journal of the Audio Engineering Society
dc.relation.isbasedon	10.17743/jaes.2017.0016
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0906 Electrical and Electronic Engineering, 0915 Interdisciplinary Engineering, 1005 Communications Technologies
dc.subject.classification	Acoustics
dc.title	Robust acoustic contrast control with reduced in-situ measurement by acoustic modeling
dc.type	Journal Article
utslib.citation.volume	65
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0915 Interdisciplinary Engineering
utslib.for	1005 Communications Technologies
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.consider-herdc	false
dc.date.updated	2022-08-05T03:24:49Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	65
utslib.citation.issue	6

Abstract:

Personal audio systems generate a local sound field for a listener while attenuating the sound energy at pre-defined quiet zones. In practice, system performance is sensitive to errors in the acoustic transfer functions between the sources and the zones. Regularization is commonly used to improve robustness, however, selecting a regularization parameter is not always straightforward. In this paper a design framework for robust reproduction is proposed, combining transfer function and error modeling. The framework allows a physical perspective on the regularization required for a system, based on the bound of assumed additive or multiplicative errors, which is obtained by acoustic modeling. Acoustic contrast control is separately combined with worst-case and probability-model optimization, exploiting limited knowledge of the potential error distribution. Monte-Carlo simulations show that these approaches give increased system robustness compared to the state of the art approaches for regularization parameter estimation, and experimental results verify that robust sound zone control is achieved in the presence of loudspeaker gain errors. Furthermore, by applying the proposed framework, in-situ transfer function measurements were reduced to a single measurement per loudspeaker, per zone, with limited acoustic contrast degradation of less than 2 dB over 100-3000 Hz compared to the fully measured regularized case.

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http://hdl.handle.net/10453/159642