基于分段生成的钢琴曲自动作曲模型，支持用户交互式创作。
Segment-Factorized Full-Song Generation on Symbolic Piano Music
- 将乐曲分解为片段，通过选择性注意力逐段生成。
- 相比以往方法，生成质量更高且效率更优。
- 适合音乐创作者与AI协同作曲，支持结构自定义。
我们提出分段全曲生成模型（SFS），用于符号化钢琴曲的全曲生成。该模型接受用户提供的歌曲结构和可选的简短种子片段，作为主旋律的锚点以构建整首乐曲。通过将乐曲因子化为多个片段，并利用对相关片段的选择性注意力机制进行生成，SFS在质量和效率上均优于先前方法。为验证其在人机协作中的适用性，我们进一步将SFS封装为网页应用，支持用户在钢琴卷帘界面中通过可定制的结构和灵活的段落顺序进行迭代式音乐共创。
原文摘要 · Abstract (English)
We propose the Segmented Full-Song Model (SFS) for symbolic full-song generation. The model accepts a user-provided song structure and an optional short seed segment that anchors the main idea around which the song is developed. By factorizing a song into segments and generating each one through selective attention to related segments, the model achieves higher quality and efficiency compared to prior work. To demonstrate its suitability for human-AI interaction, we further wrap SFS into a web application that enables users to iteratively co-create music on a piano roll with customizable structures and flexible ordering.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。