Abstract
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.
Abstract (translated)
音乐领域的生成式人工智能已经取得了显著的进步，但仍然不及自然语言处理领域取得的重大成就，这主要是由于可用的音乐数据有限。知识驱动的方法已被证明可以提高音乐生成模型的表现，即使只是整合了少量的音乐知识也是如此。本文旨在通过利用全面的音乐理论来增强AI驱动的音乐创作任务（如算法作曲和风格转换），这些任务通常需要现有技术下大量的手动工作才能完成。我们提出了一种新的自动构建音乐词汇表的模型，该模型生成了一个名为CompLex的词汇库，包含由9个手动输入类别关键词和5个句子提示模板产生的37,432个项目。此外，还提出了一个新的多代理算法来自动检测并减轻幻觉问题。在三个最先进的文本到音乐生成模型上进行测试时，CompLex显示了显著的表现提升，这些模型涵盖了符号基和音频基方法。我们从完整度、准确性、非冗余性和可执行性等方面评估了CompLex，确认其具备了一个有效词汇表的关键特性。
URL
https://arxiv.org/abs/2508.19603
