MuseLeader: Toward Music Editing through Time-series Semantic Parameters Control using Large Language Model

Designing control methods in music generation systems is essential for music generation along with user preferences. In particular, parameter control provides an effective means of adjusting the atmosphere of generated music, such as "brightness." Additionally, some music generation systems allow users to specify transitions in atmospheric intensity. However, parameter control is constrained by the frame problem, where users can only manipulate the parameters predefined by the system. To overcome this limitation, this study proposes an approach that leverages large language models (LLMs) to allow users to define parameter meanings through text. We also introduce MuseLeader, a working music composition system. This is equipped with a graphical user interface supporting customization of semantically defined time-series parameters. User studies indicate that parameters with clear semantic definitions (e.g. "powerful," "robotic") can be effectively controlled according to user intent. Additionally, some users refine their expressive intentions through changing parameter axis. For further advancements, it is essential not only to enhance the inference capabilities of LLMs but also to explore multimodal inputs beyond text to improve the interpretation of complex and nuanced musical concepts.