Feb. 13, 2024, 5:48 a.m. | Antonio R\'ios-Vila Jorge Calvo-Zaragoza Thierry Paquet

cs.CV updates on arXiv.org arxiv.org

State-of-the-art end-to-end Optical Music Recognition (OMR) has, to date, primarily been carried out using monophonic transcription techniques to handle complex score layouts, such as polyphony, often by resorting to simplifications or specific adaptations. Despite their efficacy, these approaches imply challenges related to scalability and limitations. This paper presents the Sheet Music Transformer, the first end-to-end OMR model designed to transcribe complex musical scores without relying solely on monophonic strategies. Our model employs a Transformer-based image-to-sequence framework that predicts score transcriptions …

art beyond challenges cs.cv cs.sd eess.as imply limitations music optical paper recognition scalability state transcription transformer

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