MelodyT5: A Unified Score-to-Score Transformer for Symbolic Music Processing
Shangda Wu, Yashan Wang, Xiaobing Li, Feng Yu, Maosong Sun

TL;DR
MelodyT5 introduces a unified transformer framework for symbolic music tasks, leveraging multi-task transfer learning on a large curated dataset to improve performance across diverse music processing applications.
Contribution
It presents MelodyT5, a novel encoder-decoder model that unifies multiple symbolic music tasks as score-to-score transformations, trained on a large dataset for improved versatility.
Findings
Superior performance in symbolic music tasks via multi-task transfer learning
Effective handling of data-scarce tasks in symbolic music processing
Challenging task-specific paradigms with a unified model and dataset
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMusic and Audio Processing · Music Technology and Sound Studies · Speech and Audio Processing
