# Note Value Recognition for Piano Transcription Using Markov Random Fields

## TL;DR

This paper introduces a statistical approach using Markov random fields for more accurate note value recognition in piano transcription, effectively handling deviations in performance durations and capturing voice structures.

## Contribution

It proposes a novel context-tree and performance model combination that improves note value estimation accuracy and automatically learns voice structures without supervision.

## Key findings

- Reduces average error rate by ~40% compared to previous methods.
- Score model has a greater impact than the performance model.
- Automatically captures voice structure through unsupervised learning.

## Abstract

This paper presents a statistical method for use in 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 show 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.