Unifying Symbolic Music Arrangement: Track-Aware Reconstruction and Structured Tokenization

We present a unified framework for automatic multitrack music arrangement that enables a single pre-trained symbolic music model to handle diverse arrangement scenarios, including reinterpretation, simplification, and additive generation. At its core is a segment-level reconstruction objective operating on token-level disentangled content and style, allowing for flexible any-to-any instrumentation transformations at inference time. To support track-wise modeling, we introduce REMI-z, a structured tokenization scheme for multitrack symbolic music that enhances modeling efficiency and effectiveness for both arrangement tasks and unconditional generation. Our method outperforms task-specific state-of-the-art models on representative tasks in different arrangement scenarios---band arrangement, piano reduction, and drum arrangement, in both objective metrics and perceptual evaluations. Taken together, our framework demonstrates strong generality and suggests broader applicability in symbolic music-to-music transformation.

Authors

• Longshen Ou
• Jingwei Zhao
• Ziyu Wang - Huawei Technologies Ltd.
• Gus Xia
• Qihao Liang
• Torin Hopkins
• Ye Wang Mitsubishi Electric Research Labs

Keywords

No keywords are indexed for this paper.