用可编辑乐谱生成长歌，效率与质量双突破。
Via Score to Performance: Efficient Human-Controllable Long Song Generation with Bar-Level Symbolic Notation
- 基于小节级符号化表示，支持人类直接编辑乐谱
- 小模型实现现有最佳生成时长与听觉质量
- 适合需要可控创作的音乐人或作曲辅助场景
歌曲生成是音乐AIGC中最具挑战性的问题，但现有方法仍存在可控性、泛化能力、感知质量和持续时间四大局限。我们认为这些缺陷主要源于当前模型试图从原始音频中直接学习乐理知识，而这一任务对现有模型而言仍过于困难。为此，我们提出首个面向可编辑符号乐谱的歌曲生成模型——小节级人工智能作曲助手（BACH）。BACH采用专为分层歌曲结构设计的分词策略与符号生成流程，在生成效率、长度和听觉质量上取得显著提升。实验表明，尽管模型规模较小，BACH在所有公开报告的歌曲生成系统中达到新的SOTA，甚至超越SunO等商业方案。人类评估进一步证实其在多项主观指标上的优势。
原文摘要 · Abstract (English)
Song generation is regarded as the most challenging problem in music AIGC; nonetheless, existing approaches have yet to fully overcome four persistent limitations: controllability, generalizability, perceptual quality, and duration. We argue that these shortcomings stem primarily from the prevailing paradigm of attempting to learn music theory directly from raw audio, a task that remains prohibitively difficult for current models. To address this, we present Bar-level AI Composing Helper (BACH), the first model explicitly designed for song generation through human-editable symbolic scores. BACH introduces a tokenization strategy and a symbolic generative procedure tailored to hierarchical song structure. Consequently, it achieves substantial gains in the efficiency, duration, and perceptual quality of song generation. Experiments demonstrate that BACH, with a small model size, establishes a new SOTA among all publicly reported song generation systems, even surpassing commercial solutions such as Suno. Human evaluations further confirm its superiority across multiple subjective metrics.
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