Center for Computer Research in Music and Acoustics

Abstract: Recently, generative AI has achieved impressive results in music generation. Yet, the challenge remains: how can these models be meaningfully applied in real-world music creation, for both professional and amateur musicians? We argue that what’s missing is an interpretable generative architecture—one that captures music concepts and their relations, which can be so finely nuanced that they defy straightforward description. In this talk, I will explore various approaches to creating such an architecture, demonstrating how it enhances control and interaction in music generation.