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ARCAA: a framework to analyse the artefact ecology in computer music performance

ARCAA: a framework to analyse the artefact ecology in computer music performance

Masu, Raul, Bettega, Mela, Correia, Nuno N. ORCID: 0000-0002-9999-9162, Romão, Teresa and Morreale, Fabio (2019) ARCAA: a framework to analyse the artefact ecology in computer music performance. In: ARTECH 2019: Proceedings of the 9th International Conference on Digital and Interactive Arts. ACM, New York, USA, pp. 1-9. ISBN 978-1450372503 (doi:https://doi.org/10.1145/3359852.3359870)

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Abstract

This paper presents ARCAA (Actors, Role, Context, Activity, Artefacts), a framework that supports designers in understanding the artefact ecology in the music performance scenario, in particular, allowing to frame the role of the different actors. The ARCAA framework is the result of the combination of two different areas of HCI: artefact ecology concept, and design framework for digital musical instruments. The model borrows three categories from MINUET an established design framework and rethinks them from an ecological perspective. In ARCAA, these three categories are used as three lenses to connect each human actor to her artefact ecology. Finally, the framework allows comparing how the various artefacts create connections among the different people involved. The second part of the paper describes a case study that shows a practical adoption of the framework.

Item Type: Conference Proceedings
Title of Proceedings: ARTECH 2019: Proceedings of the 9th International Conference on Digital and Interactive Arts
Additional Information: ARTECH 2019, October 23–25, 2019, Braga, Portugal.
Uncontrolled Keywords: digital music performance, artefact ecology, design framework
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > Department of Computing & Information Systems
Related URLs:
Last Modified: 28 Feb 2020 16:11
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/25893

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