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.

翻译：音乐创作是一个迭代过程，每个阶段都需要不同的方法。然而，现有AI音乐系统在协调多个子系统满足多样化需求方面存在不足。为填补这一空白，我们提出了Loop Copilot——一个新颖的系统，使用户能够通过交互式多轮对话界面生成并迭代优化音乐。该系统利用大语言模型解读用户意图，并选择适当的AI模型执行任务。每个后端模型专攻特定任务，其输出结果被整合以符合用户需求。为确保音乐连贯性，关键属性存储于集中式表格中。我们通过半结构化访谈与问卷评估了所提系统的有效性，不仅展示了其在辅助音乐创作方面的实用性，更揭示了其更广泛的应用潜力。