ByteComposer: a Human-like Melody Composition Method based on Language Model Agent
<|reference_start|>ByteComposer: a Human-like Melody Composition Method based on Language Model Agent: 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.<|reference_end|>
@article{liang2024bytecomposer:,
    title={ByteComposer: a Human-like Melody Composition Method based on Language
  Model Agent},
    author={Xia Liang, Xingjian Du, Jiaju Lin, Pei Zou, Yuan Wan, Bilei Zhu},
    journal={arXiv preprint arXiv:2402.17785},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17785},
    primaryClass={cs.SD cs.AI eess.AS}
}