{"ID":2864331,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.23878","arxiv_id":"2509.23878","title":"Disentangling Score Content and Performance Style for Joint Piano Rendering and Transcription","abstract":"Expressive performance rendering (EPR) and automatic piano transcription (APT) are fundamental yet inverse tasks in music information retrieval: EPR generates expressive performances from symbolic scores, while APT recovers scores from performances. Despite their dual nature, prior work has addressed them independently. In this paper we propose a unified framework that jointly models EPR and APT by disentangling note-level score content and global performance style representations from both paired and unpaired data. Our framework is built on a transformer-based sequence-to-sequence architecture and is trained using only sequence-aligned data, without requiring fine-grained note-level alignment. To automate the rendering process while ensuring stylistic compatibility with the score, we introduce an independent diffusion-based performance style recommendation module that generates style embeddings directly from score content. This modular component supports both style transfer and flexible rendering across a range of expressive styles. Experimental results from both objective and subjective evaluations demonstrate that our framework achieves competitive performance on EPR and APT tasks, while enabling effective content-style disentanglement, reliable style transfer, and stylistically appropriate rendering. Demos are available at https://jointpianist.github.io/epr-apt/","short_abstract":"Expressive performance rendering (EPR) and automatic piano transcription (APT) are fundamental yet inverse tasks in music information retrieval: EPR generates expressive performances from symbolic scores, while APT recovers scores from performances. Despite their dual nature, prior work has addressed them independently...","url_abs":"https://arxiv.org/abs/2509.23878","url_pdf":"https://arxiv.org/pdf/2509.23878v1","authors":"[\"Wei Zeng\",\"Junchuan Zhao\",\"Ye Wang\"]","published":"2025-09-28T13:36:33Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"cs.AI\",\"cs.MM\",\"eess.AS\"]","methods":"[\"Diffusion Model\",\"Transformer\"]","has_code":false}