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dc.contributor.authorRagano, A
dc.contributor.authorBENETOS, E
dc.contributor.authorHines, A
dc.contributor.author11th International Conference on Quality of Multimedia Experience
dc.date.accessioned2019-05-02T12:48:50Z
dc.date.available2019-04-15
dc.date.available2019-05-02T12:48:50Z
dc.date.issued2019-06-05
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/57323
dc.description.abstractPerceived 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.en_US
dc.format.extent? - ? (3)
dc.publisher11th International Conference on Quality of Multimedia Experienceen_US
dc.titleAdapting the Quality of Experience Framework for Audio Archive Evaluationen_US
dc.typeConference Proceedingen_US
dc.rights.holder© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
pubs.notesNot knownen_US
pubs.publication-statusAccepteden_US
dcterms.dateAccepted2019-04-15
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US
qmul.funderA Machine Learning Framework for Audio Analysis and Retrieval::Royal Academy of Engineeringen_US
qmul.funderA Machine Learning Framework for Audio Analysis and Retrieval::Royal Academy of Engineeringen_US


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