Problem
Research questions and friction points this paper is trying to address.
Automatic Music Transcription
Hand Separation
Fingering Annotation
Piano Sheet Music
Statistical Models for Automatic Fingering-Annotated Piano Sheet Music Transcription
🤖 AI Summary
This study addresses the lack of hand separation and fingering annotation in automatic piano transcription by proposing an end-to-end framework that transcribes audio directly into musical scores with fingering indications. The proposed approach integrates eight statistical methods, including hidden Markov models, rule-based systems, and N-gram language models, to achieve joint hand and fingering assignment for individual notes. As the first baseline established for this task, this work demonstrates the feasibility of automated score generation. Experimental evaluation on the PIG dataset yields a hand separation accuracy of 90.8% and a joint annotation accuracy of 56.6%.
📝 Abstract
Machine learning tools have significantly aided automatic piano music transcription; however, this domain has focused primarily on accurately predicting the pitches and timings of played notes. To produce sheet music for the piano, notes must be separated into two staves, one for each hand, and good sheet music often contains fingering annotations to guide the player when sight-reading or learning fast or complex pieces. We propose 8 statistical approaches for combined hand and fingering annotation of transcribed piano notes, including baseline hidden Markov models, rule-based methods, a synthesis of existing approaches, and N-gram language models. Furthermore, we develop a pipeline for complete transcription from piano audio to fingering-annotated sheet music. Evaluations with the PIG dataset demonstrate that our Synthesis model achieves a hand separation accuracy of 90.8\% and a joint hand and finger annotation accuracy of 56.6\%. These approaches serve as a new baseline for further research into this problem, while our pipeline demonstrates the feasibility of a combined system for automated note transcription, hand separation, and fingering annotation.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Automatic Music Transcription
Fingering Annotation
Hand Separation
Statistical Models
Hidden Markov Models
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