# PKSpell: Data-Driven Pitch Spelling and Key Signature Estimation

arXiv: https://arxiv.org/abs/2107.14009 · HTML: https://mlsanity.com/paper/2107.14009/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2021.

## Built on by (most cited first, 7 of 7)

- [End-to-end Piano Performance-MIDI to Score Conversion with Transformers](https://mlsanity.com/paper/2410.00210/) (2024)
- [GraphMuse: A Library for Symbolic Music Graph Processing](https://mlsanity.com/paper/2407.12671/) (2024)
- [Perception-Inspired Graph Convolution for Music Understanding Tasks](https://mlsanity.com/paper/2405.09224/) (2024)
- [Cluster and Separate: a GNN Approach to Voice and Staff Prediction for Score Engraving](https://mlsanity.com/paper/2407.21030/) (2024)
- [Jazz Contrafact Detection](https://mlsanity.com/paper/2208.00792/) (2022)
- [EngravingGNN: A Hybrid Graph Neural Network for End-to-End Piano Score Engraving](https://mlsanity.com/paper/2509.19412/) (2025)
- [Pitch Spelling Jazz Lead Sheets, Solo Transcriptions, Classical Piano and Monophonic Scores](https://mlsanity.com/paper/2606.20198/) (2026)

## Abstract (arXiv)

We present PKSpell: a data-driven approach for the joint estimation of pitch spelling and key signatures from MIDI files. Both elements are fundamental for the production of a full-fledged musical score and facilitate many MIR tasks such as harmonic analysis, section identification, melodic similarity, and search in a digital music library. We design a deep recurrent neural network model that only requires information readily available in all kinds of MIDI files, including performances, or other symbolic encodings. We release a model trained on the ASAP dataset. Our system can be used with these pre-trained parameters and is easy to integrate into a MIR pipeline. We also propose a data augmentation procedure that helps retraining on small datasets. PKSpell achieves strong key signature estimation performance on a challenging dataset. Most importantly, this model establishes a new state-of-the-art performance on the MuseData pitch spelling dataset without retraining.