# MelodyT5: A Unified Score-to-Score Transformer for Symbolic Music Processing

arXiv: https://arxiv.org/abs/2407.02277 · HTML: https://mlsanity.com/paper/2407.02277/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2024.

## Built on by (most cited first, 12 of 12)

- [NotaGen: Advancing Musicality in Symbolic Music Generation with Large Language Model Training Paradigms](https://mlsanity.com/paper/2502.18008/) (2025)
- [Towards an AI Musician: Synthesizing Sheet Music Problems for Musical Reasoning](https://mlsanity.com/paper/2509.04059/) (2025)
- [Unifying Symbolic Music Arrangement: Track-Aware Reconstruction and Structured Tokenization](https://mlsanity.com/paper/2408.15176/) (2024)
- [LEGATO: Large-scale End-to-end Generalizable Approach to Typeset OMR](https://mlsanity.com/paper/2506.19065/) (2025)
- [Via Score to Performance: Efficient Human-Controllable Long Song Generation with Bar-Level Symbolic Notation](https://mlsanity.com/paper/2508.01394/) (2025)
- [Diffusion-based Symbolic Music Generation with Structured State Space Models](https://mlsanity.com/paper/2507.20128/) (2025)
- [Incorporating Structure and Chord Constraints in Symbolic Transformer-based Melodic Harmonization](https://mlsanity.com/paper/2512.07627/) (2025)
- [PhraseVAE and PhraseLDM: Latent Diffusion for Full-Song Multitrack Symbolic Music Generation](https://mlsanity.com/paper/2512.11348/) (2025)
- [Improving BERT for Symbolic Music Understanding Using Token Denoising and Pianoroll Prediction](https://mlsanity.com/paper/2507.04776/) (2025)
- [Amadeus: Autoregressive Model with Bidirectional Attribute Modelling for Symbolic Music](https://mlsanity.com/paper/2508.20665/) (2025)
- [Pay (Cross) Attention to the Melody: Curriculum Masking for Single-Encoder Melodic Harmonization](https://mlsanity.com/paper/2601.16150/) (2026)
- [TUTTI: Toward generalizable audio-to-score transcription via fully synthesized data](https://mlsanity.com/paper/2609.00640/) (2026)

## Abstract (arXiv)

In the domain of symbolic music research, the progress of developing scalable systems has been notably hindered by the scarcity of available training data and the demand for models tailored to specific tasks. To address these issues, we propose MelodyT5, a novel unified framework that leverages an encoder-decoder architecture tailored for symbolic music processing in ABC notation. This framework challenges the conventional task-specific approach, considering various symbolic music tasks as score-to-score transformations. Consequently, it integrates seven melody-centric tasks, from generation to harmonization and segmentation, within a single model. Pre-trained on MelodyHub, a newly curated collection featuring over 261K unique melodies encoded in ABC notation and encompassing more than one million task instances, MelodyT5 demonstrates superior performance in symbolic music processing via multi-task transfer learning. Our findings highlight the efficacy of multi-task transfer learning in symbolic music processing, particularly for data-scarce tasks, challenging the prevailing task-specific paradigms and offering a comprehensive dataset and framework for future explorations in this domain.