用专家系统识别中文简谱乐谱，自动转成可编辑音乐格式。
The Renaissance of Expert Systems: Optical Recognition of Printed Chinese Jianpu Musical Scores with Lyrics
- 结合传统视觉分析与无监督深度学习，构建可解释的识别流程。
- 在5000首民歌上实现95.1%的音符识别准确率，歌词识别达93.1%。
- 适合音乐数字化、文化遗产保护及中文音乐数据研究者使用。
大规模光学音乐识别（OMR）研究主要聚焦西方五线谱，而中文简谱及其丰富的歌词资源长期被忽视。我们提出一个模块化专家系统流水线，将印刷版带歌词的简谱乐谱转化为机器可读的MusicXML和MIDI格式，无需大量标注训练数据。方法采用自上而下的专家系统设计，利用传统计算机视觉技术（如乐句相关性、骨架分析）发挥先验知识优势，同时集成无监督深度学习模块进行图像特征嵌入。该混合策略兼顾可解释性与精度。在《中国民歌集》数据集上评估，系统成功数字化了：(i) 超过5000首仅含旋律的歌曲（>30万音符），以及 (ii) 包含歌词的精选子集，共1400余首（>10万音符）。在旋律识别上达到音符级F1=0.951，歌词对齐识别达字符级F1=0.931。
原文摘要 · Abstract (English)
Large-scale optical music recognition (OMR) research has focused mainly on Western staff notation, leaving Chinese Jianpu (numbered notation) and its rich lyric resources underexplored. We present a modular expert-system pipeline that converts printed Jianpu scores with lyrics into machine-readable MusicXML and MIDI, without requiring massive annotated training data. Our approach adopts a top-down expert-system design, leveraging traditional computer-vision techniques (e.g., phrase correlation, skeleton analysis) to capitalize on prior knowledge, while integrating unsupervised deep-learning modules for image feature embeddings. This hybrid strategy strikes a balance between interpretability and accuracy. Evaluated on The Anthology of Chinese Folk Songs, our system massively digitizes (i) a melody-only collection of more than 5,000 songs (> 300,000 notes) and (ii) a curated subset with lyrics comprising over 1,400 songs (> 100,000 notes). The system achieves high-precision recognition on both melody (note-wise F1 = 0.951) and aligned lyrics (character-wise F1 = 0.931).
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