A composite objective measure on subjective evaluation of speech enhancement algorithms

Lin, Z; Zhou, L; Qiu, X

A composite objective measure on subjective evaluation of speech enhancement algorithms

Lin, Z Zhou, L Qiu, X

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Publication Type:: Journal Article
Citation:: Applied Acoustics, 2019, 145 pp. 144 - 148
Issue Date:: 2019-02-01

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Field	Value	Language
dc.contributor.author	Lin, Z	en_US
dc.contributor.author	Zhou, L	en_US
dc.contributor.author	Qiu, X https://orcid.org/0000-0002-5181-1220	en_US
dc.date.available	2021-02-02T18:04:54Z
dc.date.issued	2019-02-01	en_US
dc.identifier.citation	Applied Acoustics, 2019, 145 pp. 144 - 148	en_US
dc.identifier.issn	0003-682X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/129857
dc.description.abstract	© 2018 Elsevier Ltd Speech enhancement algorithms is to improve speech quality, naturalness and intelligibility by eliminating the background noise and improving signal to noise ratio. There are several objective measures predicting the quality of noisy speech enhanced by noise suppression algorithms, and different objective measures capture different characteristics of the degraded signal. In this paper, the multiple linear regression analysis is used to obtain a composite measure which has high correlation with subjective tests, and the performance of several speech enhancement algorithms under car noise conditions is compared. The uncertainty of the results of the proposed measures on different speech enhancement algorithms is analyzed, and the reliability of the results is discussed.	en_US
dc.relation.ispartof	Applied Acoustics	en_US
dc.relation.isbasedon	10.1016/j.apacoust.2018.10.002	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Acoustics	en_US
dc.title	A composite objective measure on subjective evaluation of speech enhancement algorithms	en_US
dc.type	Journal Article
utslib.citation.volume	145	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0203 Classical Physics	en_US
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	1201 Architecture	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.publication-status	Published	en_US
pubs.volume	145	en_US

Abstract:

© 2018 Elsevier Ltd Speech enhancement algorithms is to improve speech quality, naturalness and intelligibility by eliminating the background noise and improving signal to noise ratio. There are several objective measures predicting the quality of noisy speech enhanced by noise suppression algorithms, and different objective measures capture different characteristics of the degraded signal. In this paper, the multiple linear regression analysis is used to obtain a composite measure which has high correlation with subjective tests, and the performance of several speech enhancement algorithms under car noise conditions is compared. The uncertainty of the results of the proposed measures on different speech enhancement algorithms is analyzed, and the reliability of the results is discussed.

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http://hdl.handle.net/10453/129857