[2607.01849] Decomposer: Learning to Decompile Symbolic Music to Programs

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Computer Science > Machine Learning

arXiv:2607.01849 (cs)

[Submitted on 2 Jul 2026]

Title:Decomposer: Learning to Decompile Symbolic Music to Programs

Authors:Yewon Kim, Apurva Gandhi, David Chung, Graham Neubig, Chris Donahue<br>View a PDF of the paper titled Decomposer: Learning to Decompile Symbolic Music to Programs, by Yewon Kim and 4 other authors

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Abstract:Musical performance involves executing a set of high-level musical instructions, yet recovering those instructions from the performance is a challenging inverse problem. We present Decomposer, a post-training framework for symbolic music decompilation: the task of recovering executable, editable music programs from symbolic music. We instantiate the task as MIDI-to-Strudel decompilation, where the model takes symbolic MIDI as input and produces a program in Strudel, a music programming language, that reconstructs the input when executed. The task poses two challenges: Strudel is a low-resource language with little naturally paired MIDI-code data, and optimizing faithful reconstruction of MIDI alone can collapse to unreadable note-by-note transliteration. We address these challenges in two stages. First, we construct Strudel-Synth, a synthetic corpus of paired Strudel programs and rendered MIDI, and use it for supervised fine-tuning. Second, we refine the model with reinforcement learning on unpaired MIDI, optimizing rewards for both MIDI reconstruction faithfulness and code readability. Our evaluation across synthetic and real-world MIDI benchmarks shows that Decomposer achieves substantially higher MIDI reconstruction faithfulness than closed-source LLMs while producing more readable and diverse code than the heuristic converter.

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Subjects:

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Sound (cs.SD)

Cite as:<br>arXiv:2607.01849 [cs.LG]

(or<br>arXiv:2607.01849v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.01849

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Yewon Kim [view email]<br>[v1]<br>Thu, 2 Jul 2026 08:09:52 UTC (686 KB)

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