Rongfeng Li

Fan Bu, Rongfeng Li, Zijin Li et al.

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).

Yifan Xie, Rongfeng Li

Today's research on Chinese music technology is mainly focused on three aspects: data collection, music deconstruction, and music reconstruction. In this paper, a general method is proposed to collect Chinese music in the form of numbered musical notation, and a Dizi dataset is collected using this method. Based on the collected Dizi dataset, we conduct research on the Dizi music styles of the Northern school and the Southern School. Characteristics include melody and playing techniques of the two different music styles are deconstructed. A reconstruction example, music style transfer which includes melody transfer and playing techniques transfer is given and audience evaluation is done to evaluate the reconstruction results.

Yifan Xie, Rongfeng Li

Music generation has always been a hot topic. When discussing symbolic music, melody or harmonies are usually seen as the only generating targets. But in fact, playing techniques are also quite an important part of the music. In this paper, we discuss the playing techniques generation problem by seeing it as a tagging problem. We propose a model that can use both the current data and external knowledge. Experiments were carried out by applying the proposed model in Chinese bamboo flute music, and results show that our method can make generated music more lively.