{"ID":6267061,"CreatedAt":"2026-07-10T01:11:38.759438437Z","UpdatedAt":"2026-07-13T01:02:08.706470581Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.08168","arxiv_id":"2607.08168","title":"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.","short_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 sett...","url_abs":"https://arxiv.org/abs/2607.08168","url_pdf":"https://arxiv.org/pdf/2607.08168v1","authors":"[\"Simon Rouard\",\"Michael Krause\",\"Axel Roebel\",\"Carl-Johann Simon-Gabriel\",\"Alexandre Défossez\"]","published":"2026-07-09T07:12:30Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"cs.LG\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}