Via Score to Performance: Efficient Human-Controllable Long Song Generation with Bar-Level Symbolic Notation

• URL: http://arxiv.org/abs/2508.01394v1
• Date: Sat, 02 Aug 2025 14:58:34 GMT
• Title: Via Score to Performance: Efficient Human-Controllable Long Song Generation with Bar-Level Symbolic Notation
• Abstract summary: Song generation is regarded as the most challenging problem in music AIGC.<n>We present Bar-level AI Composing Helper (BACH), the first model explicitly designed for song generation through human-editable symbolic scores.<n>BACH introduces a tokenization strategy and a symbolic generative procedure tailored to hierarchical song structure.
• Score: 5.793988399290161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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