Reflection Across AI-based Music Composition

Abstract

Reflection is fundamental to creative practice. However, the plurality of ways in which people reflect when using AI Generated Content (AIGC) is underexplored. This paper takes AI-based music composition as a case study to explore how artist-researcher composers reflected when integrating AIGC into their music composition process. The AI tools explored range from Markov Chains for music generation to Variational Auto-Encoders for modifying timbre. We used a novel method where our composers would pause and reflect back on screenshots of their composing after every hour, using this documentation to write first-person accounts showcasing their subjective viewpoints on their experience. We triangulate the first-person accounts with interviews and questionnaire measures to contribute descriptions on how the composers reflected. For example, we found that many composers reflect on future directions in which to take their music whilst curating AIGC. Our findings contribute to supporting future explorations on reflection in creative HCI contexts.