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Machine learning is challenging the way we make music. Although research indeep generative models has dramatically improved the capability and fluency ofmusic models, recent work has shown that it can be challenging for humans topartner with this new class of algorithms. In this paper, we present findingson what 13 musician/developer teams, a total of 61 users, needed whenco-creating a song with AI, the challenges they faced, and how they leveragedand repurposed existing characteristics of AI to overcome some of thesechallenges. Many teams adopted modular approaches, such as independentlyrunning multiple smaller models that align with the musical building blocks ofa song, before re-combining their results. As ML models are not easilysteerable, teams also generated massive numbers of samples and curated thempost-hoc, or used a range of strategies to direct the generation, oralgorithmically ranked the samples. Ultimately, teams not only had to managethe "flare and focus" aspects of the creative process, but also juggle themwith a parallel process of exploring and curating multiple ML models andoutputs. These findings reflect a need to design machine learning-powered musicinterfaces that are more decomposable, steerable, interpretable, and adaptive,which in return will enable artists to more effectively explore how AI canextend their personal expression.

AI Song Contest: Human-AI Co-Creation in Songwriting