ByteComposer: a Human-like Melody Composition Method based on Language Model Agent

• URL: http://arxiv.org/abs/2402.17785v2
• Date: Thu, 7 Mar 2024 00:32:27 GMT
• Title: ByteComposer: a Human-like Melody Composition Method based on Language Model Agent
• Authors: Xia Liang, Xingjian Du, Jiaju Lin, Pei Zou, Yuan Wan, Bilei Zhu
• Abstract summary: Large Language Models (LLM) have shown encouraging progress in multimodal understanding and generation tasks. We propose ByteComposer, an agent framework emulating a human's creative pipeline in four separate steps. We conduct extensive experiments on GPT4 and several open-source large language models, which substantiate our framework's effectiveness.
• Score: 11.792129708566598
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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