# Composer Style-specific Symbolic Music Generation Using Vector Quantized Discrete Diffusion Models

arXiv: https://arxiv.org/abs/2310.14044 · HTML: https://mlsanity.com/paper/2310.14044/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Audio, speech and music, one of 14 directions in Diffusion models.
- Builds on 17 of its 25 references that are in the map.
- First posted on arXiv 21 Oct 2023.

## Placed in

- Diffusion models / [Audio, speech and music](https://mlsanity.com/diffusion/audio-speech-and-music/)

## Builds on (most cited first, 17 of 17)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://mlsanity.com/paper/2111.14822/) (2021)
- [Tackling the Generative Learning Trilemma with Denoising Diffusion GANs](https://mlsanity.com/paper/2112.07804/) (2021)
- [Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset](https://mlsanity.com/paper/1810.12247/) (2018)
- [Symbolic Music Generation with Diffusion Models](https://mlsanity.com/paper/2103.16091/) (2021)
- [A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music](https://mlsanity.com/paper/1803.05428/) (2018)
- [ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis](https://mlsanity.com/paper/2108.08827/) (2021)
- [EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation](https://mlsanity.com/paper/2108.01374/) (2021)
- [Encoding Musical Style with Transformer Autoencoders](https://mlsanity.com/paper/1912.05537/) (2019)
- [Symbolic Music Genre Transfer with CycleGAN](https://mlsanity.com/paper/1809.07575/) (2018)
- [Symbolic Music Loop Generation with Neural Discrete Representations](https://mlsanity.com/paper/2208.05605/) (2022)
- [Generating symbolic music using diffusion models](https://mlsanity.com/paper/2303.08385/) (2023)

## Built on by (most cited first, 9 of 9)

- [Mamba-Diffusion Model with Learnable Wavelet for Controllable Symbolic Music Generation](https://mlsanity.com/paper/2505.03314/) (2025)
- [Composer Vector: Style-steering Symbolic Music Generation in a Latent Space](https://mlsanity.com/paper/2604.03333/) (2026)
- [LZMidi: Compression-Based Symbolic Music Generation](https://mlsanity.com/paper/2503.17654/) (2025)
- [Efficient Long-Sequence Diffusion Modeling for Symbolic Music Generation](https://mlsanity.com/paper/2603.00576/) (2026)
- [Conditional Diffusion as Latent Constraints for Controllable Symbolic Music Generation](https://mlsanity.com/paper/2511.07156/) (2025)
- [Pay (Cross) Attention to the Melody: Curriculum Masking for Single-Encoder Melodic Harmonization](https://mlsanity.com/paper/2601.16150/) (2026)
- [D3PIA: A Discrete Denoising Diffusion Model for Piano Accompaniment Generation From Lead sheet](https://mlsanity.com/paper/2602.03523/) (2026)
- [Fusing Memory and Attention: A study on LSTM, Transformer and Hybrid Architectures for Symbolic Music Generation](https://mlsanity.com/paper/2603.21282/) (2026)
- [Learning Jazz Pianist Style with Cross-Attention Conditioning](https://mlsanity.com/paper/2610.02918/) (2026)

## Abstract (arXiv)

Emerging Denoising Diffusion Probabilistic Models (DDPM) have become increasingly utilised because of promising results they have achieved in diverse generative tasks with continuous data, such as image and sound synthesis. Nonetheless, the success of diffusion models has not been fully extended to discrete symbolic music. We propose to combine a vector quantized variational autoencoder (VQ-VAE) and discrete diffusion models for the generation of symbolic music with desired composer styles. The trained VQ-VAE can represent symbolic music as a sequence of indexes that correspond to specific entries in a learned codebook. Subsequently, a discrete diffusion model is used to model the VQ-VAE's discrete latent space. The diffusion model is trained to generate intermediate music sequences consisting of codebook indexes, which are then decoded to symbolic music using the VQ-VAE's decoder. The evaluation results demonstrate our model can generate symbolic music with target composer styles that meet the given conditions with a high accuracy of 72.36%. Our code is available at https://github.com/jinchengzhanggg/VQVAE-Diffusion.