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  5. Adapting the Quality of Experience Framework for Audio Archive Evaluation
 
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Adapting the Quality of Experience Framework for Audio Archive Evaluation

Author(s)
Ragano, Alessandro  
Benetos, Emmanouil  
Hines, Andrew  
Uri
http://hdl.handle.net/10197/11366
Date Issued
2019-06-07
Date Available
2020-05-05T14:28:51Z
Abstract
Perceived quality of historical audio material that is subjected to digitisation and restoration is typically evaluated by individual judgements or with inappropriate objective quality models. This paper presents a Quality of Experience (QoE) framework for predicting perceived audio quality of sound archives. The approach consists in adapting concepts used in QoE evaluation to digital audio archives. Limitations of current objective quality models employed in audio archives are provided and reasons why a QoE-based framework can overcome these limitations are discussed. This paper shows that applying a QoE framework to audio archives is feasible and it helps to identify the stages, stakeholders and models for a QoE centric approach.
Sponsorship
Science Foundation Ireland
Other Sponsorship
RAEng Research Fellowship
Turing Fellowship
Type of Material
Conference Publication
Publisher
IEEE
Copyright (Published Version)
2019 IEEE
Subjects

Quality of experience...

Digital audio archive...

Digital audio restora...

DOI
10.1109/QoMEX.2019.8743302
Web versions
https://www.qomex2019.de/
Language
English
Status of Item
Peer reviewed
Journal
2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX)
Conference Details
The 11th International Conference on Quality of Multimedia Experience (QoMEX 2019), Berlin, Germany, 5-7 June 2019
ISBN
9781538682128
ISSN
2472-7814
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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AR__Audio_Archives_QoMEX19_Short_Paper__Copy_-2.pdf

Size

545.22 KB

Format

Adobe PDF

Checksum (MD5)

e5ec1e0b3f96f34208dd865e8c038fe3

Owning collection
Computer Science Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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