# Symbotunes: unified hub for symbolic music generative models

arXiv: https://arxiv.org/abs/2410.20515 · HTML: https://mlsanity.com/paper/2410.20515/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 27 Oct 2024.

## Builds on (most cited first, 3 of 3)

- [A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music](https://mlsanity.com/paper/1803.05428/) (2018)
- [Music transcription modelling and composition using deep learning](https://mlsanity.com/paper/1604.08723/) (2016)
- [miditok: A Python package for MIDI file tokenization](https://mlsanity.com/paper/2310.17202/) (2023)

## Built on by (most cited first, 1 of 1)

- [Frechet Music Distance: A Metric For Generative Symbolic Music Evaluation](https://mlsanity.com/paper/2412.07948/) (2024)

## Abstract (arXiv)

Implementations of popular symbolic music generative models often differ significantly in terms of the libraries utilized and overall project structure. Therefore, directly comparing the methods or becoming acquainted with them may present challenges. To mitigate this issue we introduce Symbotunes, an open-source unified hub for symbolic music generative models. Symbotunes contains modern Python implementations of well-known methods for symbolic music generation, as well as a unified pipeline for generating and training.