# Symbolic Music Loop Generation with Neural Discrete Representations

arXiv: https://arxiv.org/abs/2208.05605 · HTML: https://mlsanity.com/paper/2208.05605/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2022.

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

- [The Ethical Implications of Generative Audio Models: A Systematic Literature Review](https://mlsanity.com/paper/2307.05527/) (2023)
- [Composer Style-specific Symbolic Music Generation Using Vector Quantized Discrete Diffusion Models](https://mlsanity.com/paper/2310.14044/) (2023)
- [SYMPLEX: Controllable Symbolic Music Generation using Simplex Diffusion with Vocabulary Priors](https://mlsanity.com/paper/2405.12666/) (2024)
- [LooPy: A Research-Friendly Mix Framework for Music Information Retrieval on Electronic Dance Music](https://mlsanity.com/paper/2305.01051/) (2023)
- [Combinatorial music generation model with song structure graph analysis](https://mlsanity.com/paper/2312.15400/) (2023)
- [LoopGen: Training-Free Loopable Music Generation](https://mlsanity.com/paper/2504.04466/) (2025)
- [Learning Normal Patterns in Musical Loops](https://mlsanity.com/paper/2505.23784/) (2025)

## Abstract (arXiv)

Since most of music has repetitive structures from motifs to phrases, repeating musical ideas can be a basic operation for music composition. The basic block that we focus on is conceptualized as loops which are essential ingredients of music. Furthermore, meaningful note patterns can be formed in a finite space, so it is sufficient to represent them with combinations of discrete symbols as done in other domains. In this work, we propose symbolic music loop generation via learning discrete representations. We first extract loops from MIDI datasets using a loop detector and then learn an autoregressive model trained by discrete latent codes of the extracted loops. We show that our model outperforms well-known music generative models in terms of both fidelity and diversity, evaluating on random space. Our code and supplementary materials are available at https://github.com/sjhan91/Loop_VQVAE_Official.