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 in a standard digital music encoding format from input images. Our model has been tested on two polyphonic music datasets and has proven capable of handling these intricate music structures effectively. The experimental outcomes not only indicate the competence of the model, but also show that it is better than the state-of-the-art methods, thus contributing to advancements in end-to-end OMR transcription.

本研究提出了Sheet Music Transformer模型，这是首个能够在处理复杂音乐乐谱时不仅依赖于单音技术的端到端光学乐谱识别模型。模型基于Transformer的图像到序列框架，能够从输入图像中预测出标准的数字音乐编码格式的乐谱转录。实验结果表明，该模型不仅表现出了高度的能力，而且也优于目前最先进的方法，从而为端到端光学乐谱识别的进步做出了贡献。

乐谱变换器：端到端的光学乐谱识别超越单声部转录