Design of a class of zero attraction based sparse adaptive feedback cancellers for assistive listening devices

Bhattacharjee, SS; Pradhan, S; George, NV

Design of a class of zero attraction based sparse adaptive feedback cancellers for assistive listening devices

Bhattacharjee, SS Pradhan, S

George, NV

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Applied Acoustics, 2021, 173, pp. 1-7
Issue Date:: 2021-02-01

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Field	Value	Language
dc.contributor.author	Bhattacharjee, SS
dc.contributor.author	Pradhan, S https://orcid.org/0000-0002-7463-0932
dc.contributor.author	George, NV
dc.date.accessioned	2022-04-12T22:54:34Z
dc.date.available	2022-04-12T22:54:34Z
dc.date.issued	2021-02-01
dc.identifier.citation	Applied Acoustics, 2021, 173, pp. 1-7
dc.identifier.issn	0003-682X
dc.identifier.issn	1872-910X
dc.identifier.uri	http://hdl.handle.net/10453/156153
dc.description.abstract	Acoustic feedback is a frequently encountered problem in assistive listening devices (ALDs). Feedback paths in ALDs are typically sparse in nature and sparsity aware adaptive feedback cancellers can improve perceived audio quality under such scenarios. In an endeavour to improve the feedback canceller performance, a decorrelated polynomial zero attraction (DPZA) normalized least mean square (NLMS) feedback canceller is proposed in this paper. DPZA-NLMS algorithm is seen to have higher computational complexity. Hence, in an attempt to reduce computational complexity, a decorrelated l0-NLMS (D-l0-NLMS) and a decorrelated Versoria zero attraction NLMS (DVZA-NLMS) based feedback canceller are also proposed. Feedback canceller performance in terms of convergence and tracking performance as well as speech/audio quality and speech intelligibility is compared. In addition, computational complexity and memory requirements of the algorithms are also compared thus providing a hearing aid designer with better trade off choices between computational requirements and feedback cancellation performance.
dc.language	English
dc.publisher	Elsevier
dc.relation.ispartof	Applied Acoustics
dc.relation.isbasedon	10.1016/j.apacoust.2020.107683
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0203 Classical Physics, 0913 Mechanical Engineering, 1201 Architecture
dc.subject.classification	Acoustics
dc.title	Design of a class of zero attraction based sparse adaptive feedback cancellers for assistive listening devices
dc.type	Journal Article
utslib.citation.volume	173
utslib.for	0203 Classical Physics
utslib.for	0913 Mechanical Engineering
utslib.for	1201 Architecture
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	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-04-12T22:54:33Z
pubs.publication-status	Published
pubs.volume	173

Abstract:

Acoustic feedback is a frequently encountered problem in assistive listening devices (ALDs). Feedback paths in ALDs are typically sparse in nature and sparsity aware adaptive feedback cancellers can improve perceived audio quality under such scenarios. In an endeavour to improve the feedback canceller performance, a decorrelated polynomial zero attraction (DPZA) normalized least mean square (NLMS) feedback canceller is proposed in this paper. DPZA-NLMS algorithm is seen to have higher computational complexity. Hence, in an attempt to reduce computational complexity, a decorrelated l0-NLMS (D-l0-NLMS) and a decorrelated Versoria zero attraction NLMS (DVZA-NLMS) based feedback canceller are also proposed. Feedback canceller performance in terms of convergence and tracking performance as well as speech/audio quality and speech intelligibility is compared. In addition, computational complexity and memory requirements of the algorithms are also compared thus providing a hearing aid designer with better trade off choices between computational requirements and feedback cancellation performance.

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http://hdl.handle.net/10453/156153