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

TL;DR

This paper explores how musician/developer teams co-create music with AI, highlighting challenges faced and strategies used to manage AI-generated content, emphasizing the need for more controllable and interpretable music AI interfaces.

Contribution

It provides empirical insights into collaborative AI-driven songwriting processes and proposes design directions for more effective AI music tools.

Findings

Teams used modular approaches with multiple models

Curated and ranked AI outputs to guide creativity

Need for more steerable and interpretable AI interfaces

Abstract

Machine learning is challenging the way we make music. Although research in deep generative models has dramatically improved the capability and fluency of music models, recent work has shown that it can be challenging for humans to partner with this new class of algorithms. In this paper, we present findings on what 13 musician/developer teams, a total of 61 users, needed when co-creating a song with AI, the challenges they faced, and how they leveraged and repurposed existing characteristics of AI to overcome some of these challenges. Many teams adopted modular approaches, such as independently running multiple smaller models that align with the musical building blocks of a song, before re-combining their results. As ML models are not easily steerable, teams also generated massive numbers of samples and curated them post-hoc, or used a range of strategies to direct the generation, or algorithmically ranked the samples. Ultimately, teams not only had to manage the "flare and focus" aspects of the creative process, but also juggle them with a parallel process of exploring and curating multiple ML models and outputs. These findings reflect a need to design machine learning-powered music interfaces that are more decomposable, steerable, interpretable, and adaptive, which in return will enable artists to more effectively explore how AI can extend their personal expression.