MIDI to Score Method

MIDI-to-score methods aim to automatically convert MIDI files, a common digital representation of music, into human-readable musical scores. Current research focuses on improving the accuracy and expressiveness of this conversion, particularly for complex instruments like guitar, incorporating stylistic elements like fingering and articulation, and handling multi-track arrangements. This involves employing various deep learning architectures, including transformers and diffusion models, often trained on large datasets of score-MIDI pairs. The resulting advancements have implications for music education, composition assistance, and automated music transcription, potentially streamlining workflows and expanding access to musical resources.