Reflection Across AI-based Music Composition

Re ection is fundamental to creative practice.However, the plurality of ways in which people re ect 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 re ected 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 re ect back on screenshots of their composing after every hour, using this documentation to write rst-person accounts showcasing their subjective viewpoints on their experience.We triangulate the rst-person accounts with interviews and questionnaire measures to contribute descriptions on how the composers re ected.For example, we found that many composers re ect on future directions in which to take their music whilst curating AIGC.Our ndings contribute to supporting future explorations on re ection in creative HCI contexts.