Getting Motif-ated: 从注入的动机提示中可控地生成AI音乐作品

中文摘要 AI 辅助

深度学习通过借鉴大语言模型的训练范式，已经彻底改变了符号音乐生成领域，诸如NotaGen等系统现在能够从简短提示中生成完整且风格上令人信服的古典乐谱。这些系统有望成为强大的创作伙伴，帮助音乐家探索无限的可能性。然而，当前系统在音乐本身方面几乎没有提供任何控制手段。原则上，控制手段可以内置于从零开始训练的基础模型中，但如果没有大量高质量标注数据和计算资源，这在实际中几乎不可行。因此，我们提出了MotiGen，一种对预训练符号音乐模型进行改造以使用新指令提示的方法。MotiGen将音乐动机作为结构化提示行注入，通过标量注意力偏置强化对动机标记的关注，并通过两阶段课程学习关联。第一阶段，模型从训练数据中围绕动机出现位置裁剪的聚焦片段进行学习；第二阶段，则学习包含动机的完整乐谱。我们的实验表明，在超过92.3%的生成作品中，模型能够按照提示的动机进行创作。使用多种动机生成的作品已包含在我们的示例网站中：此 https URL。

英文摘要

Deep learning has transformed symbolic music generation by borrowing the training paradigms of large language models, with systems such as NotaGen now producing complete, stylistically convincing classical scores from a short prompt. These systems could become powerful creative partners, helping musicians generate endless possibilities. However, current systems expose almost no control handles on the music itself. In principle, control handles could be built into a foundation model trained from scratch, but this is rarely practical without massive amounts of quality annotated data and compute resources. We therefore present MotiGen, a recipe for retrofitting pretrained symbolic music models to use new instruction prompts. MotiGen injects a musical motif as a structured prompt line, reinforces it with a scalar attention bias toward the motif tokens, and learns the association with a two-phase curriculum. First, it learns from focused excerpts cropped around motif occurrences in the training data, then full scores including the motifs. Our experiments show that our model composes with the prompted motif in over 92.3\% of generated pieces. Generated pieces using a variety of motifs are included in our sample site: https://motigen-site.github.io/.

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NeurIPS 2026, Creative AI Track