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Synaesthetic audio-visual sound toys in virtual reality

Synaesthetic audio-visual sound toys in virtual reality

Weinel, Jonathan ORCID: 0000-0001-5347-3897 (2021) Synaesthetic audio-visual sound toys in virtual reality. In: AM '21: Audio Mostly 2021. Association for Computing Machinery (ACM), New York, USA, pp. 135-138. ISBN 978-1450385695 (doi:

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This paper discusses the design of audio-visual sound toys in Cyberdream, a virtual reality music visualization. While an earlier version of this project for Oculus GearVR provided a journey through audio-visual environments related to 1990s rave culture, the most recent iteration for Oculus Quest provides the addition of three audio-visual sound toys, the discussion of which is the main focus of this paper. In the latest version, the user flies through synaesthetic environments, while using the interactive controllers to manipulate the audio-visual sound toys and 'paint with sound'. These toys allow the user to playfully manipulate sound and image in a way that is complementary to, and interfaces with, the audio-visual backdrop provided by the VR music visualization. Through the discussion of novel approaches to design, the project informs new strategies in the field of VR music visualizations.

Item Type: Conference Proceedings
Title of Proceedings: AM '21: Audio Mostly 2021
Additional Information: This conference was held at Trento, Italy from 1-3 September 2021.
Uncontrolled Keywords: virtual reality, sound design, VJ, visual music, visualization, video game engines
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 Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > School of Computing & Mathematical Sciences (CMS)
Faculty of Liberal Arts & Sciences > Sound-Image Research Group
Related URLs:
Last Modified: 30 Nov 2021 11:32
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None

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