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Virtual AI Jam: AI-Driven Virtual Musicians for Human-in-the-Loop Musical Improvisation

Authors: Hopkins, Torin; Jude, Alvin; Phillips, Greg; Do, Ellen Yi-Luen;

Virtual AI Jam: AI-Driven Virtual Musicians for Human-in-the-Loop Musical Improvisation

Abstract

https://aimc2023.pubpub.org/pub/9064kmvw This paper introduces an AI-driven and animated interactive musician system. Current AI-driven systems that accomplish interaction with a human musician often lack visual representation, or require building robots to play acoustic instruments. Our system provides an infrastructure for creating custom interactive musicians on a personal computer. Throughout the paper we introduce the system pipeline, our design process, feedback from semi-structured interviews with musicians, and how we addressed feedback to inform improvements and limitations for interaction. Findings suggest that many musicians desire interactive AI-driven improvisation systems that are explicitly controlled by the player as compared to free-form musical improvisation with autonomous virtual musicians. We share the interactive and animated AI musician system for public use and collective improvement, complete with an associated repository.

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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