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Note Value Recognition for Rhythm Transcription Using a Markov Random Field Model for Musical Scores and Performances of Piano Music

Abstract · Mar 23, 2017 17:07 ·

cs-ai cs-sd

Arxiv Abstract

  • Eita Nakamura
  • Kazuyoshi Yoshii
  • Simon Dixon

This paper presents a statistical method for music transcription that can estimate score times of note onsets and offsets from polyphonic MIDI performance signals. Because performed note durations can deviate largely from score-indicated values, previous methods had the problem of not being able to accurately estimate offset score times (or note values) and thus could only output incomplete musical scores. Based on observations that the pitch context and onset score times are influential on the configuration of note values, we construct a context-tree model that provides prior distributions of note values using these features and combine it with a performance model in the framework of Markov random fields. Evaluation results showed that our method reduces the average error rate by around 40 percent compared to existing/simple methods. We also confirmed that, in our model, the score model plays a more important role than the performance model, and it automatically captures the voice structure by unsupervised learning.

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