ByteComposer: a Human-like Melody Composition Method based on Language Model Agent
Abstract
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.
Cite
@article{arxiv.2402.17785,
title = {ByteComposer: a Human-like Melody Composition Method based on Language Model Agent},
author = {Xia Liang and Xingjian Du and Jiaju Lin and Pei Zou and Yuan Wan and Bilei Zhu},
journal= {arXiv preprint arXiv:2402.17785},
year = {2024}
}