Sheet Music Transformer：超越单音轨转录的端到端光学乐谱识别
Sheet Music Transformer: End-To-End Optical Music Recognition Beyond Monophonic Transcription
AI总结：
本文提出首个端到端光学乐谱识别模型Sheet Music Transformer，基于Transformer图像到序列框架，无需依赖单音轨策略即可有效转录复杂复调乐谱，实验证明其优于现有最先进方法。
摘要：
迄今为止，最先进的端到端光学乐谱识别（OMR）主要采用单音轨转录技术来处理复杂的乐谱布局（如复调），通常依赖简化或特定适配。尽管有效，这些方法在可扩展性方面存在挑战与局限。本文提出了Sheet Music Transformer，这是首个旨在不单纯依赖单音轨策略即可转录复杂乐谱的端到端OMR模型。该模型采用基于Transformer的图像到序列框架，从输入图像中预测标准数字音乐编码格式的乐谱转录。该模型已在两个复调音乐数据集上完成测试，证明其能有效处理这些复杂的音乐结构。实验结果不仅表明了该模型的能力，还显示其优于现有最先进方法，从而推动了端到端OMR转录的发展。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
