用智能体自动构建3.7万条音乐理论词库，提升生成质量
CompLex: Music Theory Lexicon Constructed by Autonomous Agents for Automatic Music Generation
- 基于9个关键词和5个模板，用多智能体自动构造音乐词库
- 在3个主流模型上显著提升生成效果，词库含37432项
- 可自动纠错，适合音乐生成、作曲辅助等场景
音乐生成中的人工智能虽有进展，但远不及自然语言处理，主要因音乐数据有限。融入音乐知识可提升生成模型性能，即使仅少量知识亦有效。本文提出一种自动构建音乐理论词库的方法，生成名为CompLex的词库，包含37,432项，仅需9个手动输入的类别关键词和5个句子模板。提出新型多智能体算法，自动检测并减少幻觉。CompLex在三个先进文本到音乐生成模型（涵盖符号与音频方法）中均表现优异。评估显示其具备完整性、准确性、非冗余性和可执行性，是高效词库的关键特征。
原文摘要 · Abstract (English)
Generative artificial intelligence in music has made significant strides, yet it still falls short of the substantial achievements seen in natural language processing, primarily due to the limited availability of music data. Knowledge-informed approaches have been shown to enhance the performance of music generation models, even when only a few pieces of musical knowledge are integrated. This paper seeks to leverage comprehensive music theory in AI-driven music generation tasks, such as algorithmic composition and style transfer, which traditionally require significant manual effort with existing techniques. We introduce a novel automatic music lexicon construction model that generates a lexicon, named CompLex, comprising 37,432 items derived from just 9 manually input category keywords and 5 sentence prompt templates. A new multi-agent algorithm is proposed to automatically detect and mitigate hallucinations. CompLex demonstrates impressive performance improvements across three state-of-the-art text-to-music generation models, encompassing both symbolic and audio-based methods. Furthermore, we evaluate CompLex in terms of completeness, accuracy, non-redundancy, and executability, confirming that it possesses the key characteristics of an effective lexicon.
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