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Ideary: Facilitating Electronic Music Creation with Generative AI

Published: 01 July 2024 Publication History

Abstract

This project explores how generative AI can assist electronic music producers in composing rhythms and melodies. Based on detailed user research with electronic music producers, a central tension in music composition is between authorial control and unpredictability. This paper presents Ideary, a concept where users explore the latent space of their custom generative AI models through an XY pad based on extensive user research. A user evaluation shows that music producers responded positively to this approach.

References

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Oliver Bown. 2021. Sociocultural and design perspectives on AI-based music production: why do we make music and what changes if ai makes it for us?Handbook of Artificial Intelligence for Music: Foundations, Advanced Approaches, and Developments for Creativity (2021), 1–20.
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Emmanuel Deruty, Maarten Grachten, Stefan Lattner, Javier Nistal, and Cyran Aouameur. 2022. On the development and practice of ai technology for contemporary popular music production. Transactions of the International Society for Music Information Retrieval 5, 1 (2022).
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Imke Grabe, Miguel Gonzalez Duque, and Jichen Zhu. 2022. Towards a framework for human-ai interaction patterns in co-creative gan applications. In Proceeding of the 3rd Workshop on Human-AI Co-Creation with Generative Models (HAI-GEN ‘22) at ACM IUI Workshops.
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Imke Grabe and Jichen Zhu. 2023. Hidden Layer Interaction: A Co-Creative Design Fiction for Generative Models. arXiv:2304.00266 (2023).
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Antonios Liapis, Christian Guckelsberger, Jichen Zhu, Casper Harteveld, Simone Kriglstein, Alena Denisova, Jeremy Gow, and Mike Preuss. 2023. Designing for Playfulness in Human-AI Authoring Tools. In Proceedings of the 18th International Conference on the Foundations of Digital Games. 1–4.
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Adam Roberts, Jesse Engel, Colin Raffel, Ian Simon, and Curtis Hawthorne. 2018. Musicvae: Creating a palette for musical scores with machine learning. Magenta (2018).
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Nao Tokui. 2020. Can GAN originate new electronic dance music genres?–Generating novel rhythm patterns using GAN with Genre Ambiguity Loss. arXiv preprint arXiv:2011.13062 (2020).
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Nao Tokui. 2020. Towards democratizing music production with AI-design of variational autoencoder-based rhythm generator as a DAW plugin. arXiv preprint arXiv:2004.01525 (2020).
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Georgios N Yannakakis, Antonios Liapis, and Constantine Alexopoulos. 2014. Mixed-initiative co-creativity. (2014).
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Haohang Zhang, Letian Xie, and Kaiyi Qi. 2021. Implement music generation with gan: A systematic review. In 2021 International Conference on Computer Engineering and Application (ICCEA). IEEE, 352–355.

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cover image ACM Conferences
DIS '24 Companion: Companion Publication of the 2024 ACM Designing Interactive Systems Conference
July 2024
501 pages
ISBN:9798400706325
DOI:10.1145/3656156
Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Published: 01 July 2024

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DIS '24: Designing Interactive Systems Conference
July 1 - 5, 2024
IT University of Copenhagen, Denmark

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