Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/34678
Appears in Collections:Communications, Media and Culture Journal Articles
Peer Review Status: Refereed
Title: Artificial Intelligence & Popular Music: SKYGGE, Flow Machines, and the Audio Uncanny Valley
Author(s): Avdeeff, Melissa
Contact Email: melissa.avdeeff@stir.ac.uk
Keywords: artificial intelligence
popular music
posthuman
creativity
uncanny valley
Issue Date: Dec-2019
Date Deposited: 14-Nov-2022
Citation: Avdeeff M (2019) Artificial Intelligence & Popular Music: SKYGGE, Flow Machines, and the Audio Uncanny Valley. <i>Arts</i>, 8 (4), Art. No.: 130. https://doi.org/10.3390/arts8040130
Abstract: This article presents an overview of the first AI-human collaborated album, Hello World, by SKYGGE, which utilizes Sony’s Flow Machines technologies. This case study is situated within a review of current and emerging uses of AI in popular music production, and connects those uses with myths and fears that have circulated in discourses concerning the use of AI in general, and how these fears connect to the idea of an audio uncanny valley. By proposing the concept of an audio uncanny valley in relation to AIPM (artificial intelligence popular music), this article offers a lens through which to examine the more novel and unusual melodies and harmonization made possible through AI music generation, and questions how this content relates to wider speculations about posthumanism, sincerity, and authenticity in both popular music, and broader assumptions of anthropocentric creativity. In its documentation of the emergence of a new era of popular music, the AI era, this article surveys: (1) The current landscape of artificial intelligence popular music focusing on the use of Markov models for generative purposes; (2) posthumanist creativity and the potential for an audio uncanny valley; and (3) issues of perceived authenticity in the technologically mediated “voice”.
DOI Link: 10.3390/arts8040130
Rights: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Licence URL(s): http://creativecommons.org/licenses/by/4.0/

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