MuScriptor: An Open Model for Multi-Instrument Music Transcription

Abstract:Existing methods for automatic music transcription are often limited to single-instrument recordings or fail on complex, real music mixes. Although previous work utilizes synthetic training data, the resulting models generalize poorly, leading to largely unusable transcription output in realistic, multi-instrument settings. In this work, we analyze the effectiveness of synthetic data for pre-training while combining it with fine-tuning on real music audio and post-training using reinforcement learning. We further introduce conditioning on instrument presence to customize transcriptions. Finally, we release MuScriptor, an open-weight multi-instrument music transcription model that works on real-world music recordings from across a diverse range of musical genres.

Sound (cs.SD); Machine Learning (cs.LG)

arXiv:2607.08168 [cs.SD]

(or arXiv:2607.08168v2 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2607.08168 Focus to learn more arXiv-issued DOI via DataCite

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