An Autoethnographic Exploration of XAI in Algorithmic Composition
This addresses the challenge of making generative music AI more interpretable and usable for musicians, though it is incremental as it builds on existing XAI research in a specific domain.
The paper tackled the problem of understanding and controlling generative music AI models by conducting an autoethnographic study of the MeasureVAE XAI model trained on Irish folk music, finding that the workflow emphasized dataset features over model features and enabled richer iterative use.
Machine Learning models are capable of generating complex music across a range of genres from folk to classical music. However, current generative music AI models are typically difficult to understand and control in meaningful ways. Whilst research has started to explore how explainable AI (XAI) generative models might be created for music, no generative XAI models have been studied in music making practice. This paper introduces an autoethnographic study of the use of the MeasureVAE generative music XAI model with interpretable latent dimensions trained on Irish folk music. Findings suggest that the exploratory nature of the music-making workflow foregrounds musical features of the training dataset rather than features of the generative model itself. The appropriation of an XAI model within an iterative workflow highlights the potential of XAI models to form part of a richer and more complex workflow than they were initially designed for.