Loop Copilot: Conducting AI Ensembles for Music Generation and Iterative
  Editing
Yixiao Zhang Akira Maezawa Gus Xia Kazuhiko Yamamoto Simon Dixon
Creating music is iterative, requiring varied methods at each stage. However,
existing AI music systems fall short in orchestrating multiple subsystems for
diverse needs. To address this gap, we introduce Loop Copilot, a novel system
that enables users to generate and iteratively refine music through an
interactive, multi-round dialogue interface. The system uses a large language
model to interpret user intentions and select appropriate AI models for task
execution. Each backend model is specialized for a specific task, and their
outputs are aggregated to meet the user's requirements. To ensure musical
coherence, essential attributes are maintained in a centralized table. We
evaluate the effectiveness of the proposed system through semi-structured
interviews and questionnaires, highlighting its utility not only in
facilitating music creation but also its potential for broader applications.