自动指法标注钢琴乐谱转录的统计模型
Statistical Models for Automatic Fingering-Annotated Piano Sheet Music Transcription
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中文总结 AI 辅助
提出8种统计方法联合进行钢琴音符手部分离与指法标注，并构建从音频到指法乐谱的完整流水线，在PIG数据集上综合模型达到90.8%手部分离和56.6%联合标注准确率。
中文摘要 AI 辅助
机器学习工具已显著促进了自动钢琴音乐转录；然而，该领域主要侧重于准确预测演奏音符的音高和时值。为了生成钢琴乐谱，音符必须被分离到两个谱表（每只手一个），且良好的乐谱通常包含指法标注，以在视奏或学习快速或复杂曲目时指导演奏者。我们提出了8种统计方法，用于对转录后的钢琴音符进行手部分离与指法标注的联合处理，包括基线隐马尔可夫模型、基于规则的方法、现有方法的综合以及N-gram语言模型。此外，我们开发了一个从钢琴音频到指法标注乐谱的完整转录流水线。使用PIG数据集的评估表明，我们的综合模型实现了90.8%的手部分离准确率和56.6%的手部与指法联合标注准确率。这些方法为对该问题的进一步研究提供了新的基线，而我们的流水线则证明了自动音符转录、手部分离和指法标注一体化系统的可行性。
英文摘要
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.
发表机构
- University of Alberta（阿尔伯塔大学）
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