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Automatic music playlist generation using affective technologies

Automatic music playlist generation using affective technologies

Griffiths, Darryl, Cunningham, Stuart and Weinel, Jonathan ORCID logoORCID: https://orcid.org/0000-0001-5347-3897 (2013) Automatic music playlist generation using affective technologies. In: Proceedings of the Fifth International Conference on Internet Technologies and Applications (ITA 13) : 10-13 September 2013. North East Wales Institute, Wrexham, Glyndŵr University, pp. 1-7. ISBN 9780946881819 ; 0946881812

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Abstract

This paper discusses how human emotion could be quantified using contextual and physiological information that has been gathered from a range of sensors, and how this data could then be used to automatically generate music playlists. I begin by discussing existing affective systems that automatically generate playlists based on human emotion. I then consider the current work in audio description analysis. A system is proposed that measures human emotion based on contextual and physiological data using a range of sensors. The sensors discussed to invoke such contextual characteristics range from temperature and light to EDA (electro dermal activity) and ECG (electrocardiogram). The concluding section describes the progress achieved so far, which includes defining datasets using a conceptual design, microprocessor electronics and data acquisition using MatLab. Lastly, there is brief discussion of future plans to develop this research.

Item Type: Conference Proceedings
Title of Proceedings: Proceedings of the Fifth International Conference on Internet Technologies and Applications (ITA 13) : 10-13 September 2013
Uncontrolled Keywords: affective computing, music playlists
Subjects: M Music and Books on Music > M Music
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group: Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Faculty of Liberal Arts & Sciences > Sound-Image Research Group
Faculty of Engineering & Science
Related URLs:
Last Modified: 04 Mar 2022 13:08
URI: http://gala.gre.ac.uk/id/eprint/34099

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