# Engraving Oriented Joint Estimation of Pitch Spelling and Local and Global Keys

arXiv: https://arxiv.org/abs/2402.10247 · HTML: https://mlsanity.com/paper/2402.10247/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2024.

## Built on by (most cited first, 3 of 3)

- [EngravingGNN: A Hybrid Graph Neural Network for End-to-End Piano Score Engraving](https://mlsanity.com/paper/2509.19412/) (2025)
- [Tonal parsimony in chord-sequence analysis: combining modulation cost and tonal vocabulary](https://mlsanity.com/paper/2606.03459/) (2026)
- [Pitch Spelling Jazz Lead Sheets, Solo Transcriptions, Classical Piano and Monophonic Scores](https://mlsanity.com/paper/2606.20198/) (2026)

## Abstract (arXiv)

We revisit the problems of pitch spelling and tonality guessing with a new algorithm for their joint estimation from a MIDI file including information about the measure boundaries. Our algorithm does not only identify a global key but also local ones all along the analyzed piece. It uses Dynamic Programming techniques to search for an optimal spelling in term, roughly, of the number of accidental symbols that would be displayed in the engraved score. The evaluation of this number is coupled with an estimation of the global key and some local keys, one for each measure. Each of the three informations is used for the estimation of the other, in a multi-steps procedure. An evaluation conducted on a monophonic and a piano dataset, comprising 216 464 notes in total, shows a high degree of accuracy, both for pitch spelling (99.5% on average on the Bach corpus and 98.2% on the whole dataset) and global key signature estimation (93.0% on average, 95.58% on the piano dataset). Designed originally as a backend tool in a music transcription framework, this method should also be useful in other tasks related to music notation processing.