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Archiving digital audio is conducted to preserve and make records accessible. However techniques for assessing the quality of experience (QoE) of sound archives are usually neglected. This paper presents a framework to assess the QoE of sound archives in an automatic fashion. The QoE influence factors, stakeholders, and audio archive degradations are described, and the above concepts are explored through a case study on the NASA Apollo audio archive. Each component of the framework is described in the audio archive life cycle based on digitization, restoration, and consumption. Insights and real-world examples are provided on why digitized and restored audio archives benefit from QoE assessment techniques similar to other multimedia applications, such as video calling and streaming services. The reasons why stakeholders, such as archivists, broadcasters, or public listeners, would benefit from the proposed framework are also provided.
Author (s): Ragano, Alessandro; Benetos, Emmanouil; Hines, Andrew
Affiliation:
University College Dublin, School of Computer Science, Ireland; Insight Centre for Data Analytics, Ireland; Queen Mary University of London, School of Electronic Engineering and Computer Science, UK; The Alan Turing Institute, UK; University College Dublin, School of Computer Science, Ireland; Insight Centre for Data Analytics, Ireland
(See document for exact affiliation information.)
Publication Date:
2022-04-06
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Ragano, Alessandro; Benetos, Emmanouil; Hines, Andrew; 2022; Automatic Quality Assessment of Digitized and Restored Sound Archives [PDF]; University College Dublin, School of Computer Science, Ireland; Insight Centre for Data Analytics, Ireland; Queen Mary University of London, School of Electronic Engineering and Computer Science, UK; The Alan Turing Institute, UK; University College Dublin, School of Computer Science, Ireland; Insight Centre for Data Analytics, Ireland; Paper ; Available from: https://aes2.org/publications/elibrary-page/?id=21562
Ragano, Alessandro; Benetos, Emmanouil; Hines, Andrew; Automatic Quality Assessment of Digitized and Restored Sound Archives [PDF]; University College Dublin, School of Computer Science, Ireland; Insight Centre for Data Analytics, Ireland; Queen Mary University of London, School of Electronic Engineering and Computer Science, UK; The Alan Turing Institute, UK; University College Dublin, School of Computer Science, Ireland; Insight Centre for Data Analytics, Ireland; Paper ; 2022 Available: https://aes2.org/publications/elibrary-page/?id=21562
@article{ragano2022automatic,
author={ragano alessandro and benetos emmanouil and hines andrew},
journal={journal of the audio engineering society},
title={automatic quality assessment of digitized and restored sound archives},
year={2022},
volume={70},
issue={4},
pages={252-270},
month={april},}