The advancing capabilities of computational systems to learn and adapt autonomously with datasets have provided new opportunities for designers and artists in their creative practice. This paper examines YomeciLand x Bunjil Place (Nguyen 2019), a playable sound-responsive installation that uses audio recognition to capture, recognise and categorise human sounds as a form of input to evolve a virtual environment of ‘artificial’ lifeforms. The potential of artificial intelligence in creative practice has recently drawn considerable interest, however our understanding of its application in sound practice is only emerging. The project is analysed in relation to three key themes: artificial intelligence for sound recognition, the ‘sounding body’ as play, and digital audiovisual composition as performance. In doing so, the research presents a framework for how artificial intelligence can aid sound recognition in a sound-responsive installation with YomeciLand x Bunjil Place shared as a case study to demonstrate this in practice.
B. M Costello 2009 Play and the experience of interactive art (Doctoral dissertation)
W. W GaverA BoucherS PenningtonB Walker 2004 Cultural probes and the value of uncertainty. interactions 11 5 53 56
J. F GemmekeD. P. W EllisD FreedmanA JansenW LawrenceR. C MooreM Ritter 2017 Audio Set: An ontology and human-labeled dataset for audio events IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 5-9 March 2017
S HersheyS ChaudhuriD. P. W EllisJ. F GemmekeA JansenR. C MooreK Wilson 2017 CNN architectures for large-scale audio classification IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 5-9 March 2017
U Nguyen 2019 YomeciLand x Bunjil Place (interactive installation)
A. J Polaine 2010 Developing a language of interactivity through the theory of play (Doctoral dissertation)
K SimonyanA Zisserman 2014 Very deep convolutional networks for large-scale image recognition arXiv preprint arXiv:1409.1556
C YuK. S BarsimQ KongB Yang 2018 Multi-level attention model for weakly supervised audio classification arXiv preprint arXiv:1803.02353