# Score Transformer: Generating Musical Score from Note-level Representation

arXiv: https://arxiv.org/abs/2112.00355 · HTML: https://mlsanity.com/paper/2112.00355/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 1 Dec 2021.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Efficient Transformers: A Survey](https://mlsanity.com/paper/2009.06732/) (2020)
- [Music Transformer](https://mlsanity.com/paper/1809.04281/) (2018)
- [Compound Word Transformer: Learning to Compose Full-Song Music over Dynamic Directed Hypergraphs](https://mlsanity.com/paper/2101.02402/) (2021)
- [This Time with Feeling: Learning Expressive Musical Performance](https://mlsanity.com/paper/1808.03715/) (2018)
- [A Comprehensive Survey on Deep Music Generation: Multi-level Representations, Algorithms, Evaluations, and Future Directions](https://mlsanity.com/paper/2011.06801/) (2020)
- [MMM : Exploring Conditional Multi-Track Music Generation with the Transformer](https://mlsanity.com/paper/2008.06048/) (2020)
- [Pop Music Transformer: Beat-based Modeling and Generation of Expressive Pop Piano Compositions](https://mlsanity.com/paper/2002.00212/) (2020)
- [A holistic approach to polyphonic music transcription with neural networks](https://mlsanity.com/paper/1910.12086/) (2019)
- [Non-Local Musical Statistics as Guides for Audio-to-Score Piano Transcription](https://mlsanity.com/paper/2008.12710/) (2020)

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

- [NotaGen: Advancing Musicality in Symbolic Music Generation with Large Language Model Training Paradigms](https://mlsanity.com/paper/2502.18008/) (2025)
- [End-to-end Piano Performance-MIDI to Score Conversion with Transformers](https://mlsanity.com/paper/2410.00210/) (2024)
- [Towards an AI Musician: Synthesizing Sheet Music Problems for Musical Reasoning](https://mlsanity.com/paper/2509.04059/) (2025)
- [Cluster and Separate: a GNN Approach to Voice and Staff Prediction for Score Engraving](https://mlsanity.com/paper/2407.21030/) (2024)
- [LooPy: A Research-Friendly Mix Framework for Music Information Retrieval on Electronic Dance Music](https://mlsanity.com/paper/2305.01051/) (2023)
- [Disentangling Score Content and Performance Style for Joint Piano Rendering and Transcription](https://mlsanity.com/paper/2509.23878/) (2025)
- [Empirical Study of Pop and Jazz Mix Ratios for Genre-Adaptive Chord Generation](https://mlsanity.com/paper/2605.04998/) (2026)

## Abstract (arXiv)

In this paper, we explore the tokenized representation of musical scores using the Transformer model to automatically generate musical scores. Thus far, sequence models have yielded fruitful results with note-level (MIDI-equivalent) symbolic representations of music. Although the note-level representations can comprise sufficient information to reproduce music aurally, they cannot contain adequate information to represent music visually in terms of notation. Musical scores contain various musical symbols (e.g., clef, key signature, and notes) and attributes (e.g., stem direction, beam, and tie) that enable us to visually comprehend musical content. However, automated estimation of these elements has yet to be comprehensively addressed. In this paper, we first design score token representation corresponding to the various musical elements. We then train the Transformer model to transcribe note-level representation into appropriate music notation. Evaluations of popular piano scores show that the proposed method significantly outperforms existing methods on all 12 musical aspects that were investigated. We also explore an effective notation-level token representation to work with the model and determine that our proposed representation produces the steadiest results.