Large Language Models (LLM) have shown encouraging progress in multimodal understanding and generation tasks. However, how to design a human-aligned and interpretable melody composition system is still under-explored. To solve this problem, we propose ByteComposer, an agent framework emulating a human's creative pipeline in four separate steps : "Conception Analysis - Draft Composition - Self-Evaluation and Modification - Aesthetic Selection". This framework seamlessly blends the interactive and knowledge-understanding features of LLMs with existing symbolic music generation models, thereby achieving a melody composition agent comparable to human creators. We conduct extensive experiments on GPT4 and several open-source large language models, which substantiate our framework's effectiveness. Furthermore, professional music composers were engaged in multi-dimensional evaluations, the final results demonstrated that across various facets of music composition, ByteComposer agent attains the level of a novice melody composer.

通过混合互动与知识理解特点将大型语言模型与现有符号音乐生成模型有机结合，我们提出了ByteComposer框架，该框架模拟了人类创作者的创作流程，通过四个独立步骤：概念分析-草稿创作-自我评估和修改-审美选择，实现了与人类创作者相媲美的旋律创作代理。我们在GPT4和几个开源的大型语言模型上进行了大量实验，实验证实了我们框架的有效性。此外，我们还邀请了专业音乐作曲家进行了多维度评估，最终结果表明在音乐创作的各个方面，ByteComposer代理达到了初学者作曲家的水平。

ByteComposer：一种基于语言模型代理的类人类旋律创作方法