# Symbolic Music Genre Transfer with CycleGAN

arXiv: https://arxiv.org/abs/1809.07575 · HTML: https://mlsanity.com/paper/1809.07575/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2018.

## Built on by (most cited first, 28 of 28)

- [MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training](https://mlsanity.com/paper/2106.05630/) (2021)
- [DadaGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models](https://mlsanity.com/paper/2107.14653/) (2021)
- [Dance Style Transfer with Cross-modal Transformer](https://mlsanity.com/paper/2208.09406/) (2022)
- [The Ethical Implications of Generative Audio Models: A Systematic Literature Review](https://mlsanity.com/paper/2307.05527/) (2023)
- [MusIAC: An extensible generative framework for Music Infilling Applications with multi-level Control](https://mlsanity.com/paper/2202.05528/) (2022)
- [Music Composition with Deep Learning: A Review](https://mlsanity.com/paper/2108.12290/) (2021)
- [A Review of Intelligent Music Generation Systems](https://mlsanity.com/paper/2211.09124/) (2022)
- [Survey on the Evaluation of Generative Models in Music](https://mlsanity.com/paper/2506.05104/) (2025)
- [Composer Style-specific Symbolic Music Generation Using Vector Quantized Discrete Diffusion Models](https://mlsanity.com/paper/2310.14044/) (2023)
- [A Comprehensive Survey for Evaluation Methodologies of AI-Generated Music](https://mlsanity.com/paper/2308.13736/) (2023)
- [CycleDRUMS: Automatic Drum Arrangement For Bass Lines Using CycleGAN](https://mlsanity.com/paper/2104.00353/) (2021)
- [Scalable Motion Style Transfer with Constrained Diffusion Generation](https://mlsanity.com/paper/2312.07311/) (2023)
- [Language Models are Drummers: Drum Composition with Natural Language Pre-Training](https://mlsanity.com/paper/2301.01162/) (2023)
- [ImprovNet -- Generating Controllable Musical Improvisations with Iterative Corruption Refinement](https://mlsanity.com/paper/2502.04522/) (2025)
- [A Two-stage Complex Network using Cycle-consistent Generative Adversarial Networks for Speech Enhancement](https://mlsanity.com/paper/2109.02011/) (2021)
- [WaveTransfer: A Flexible End-to-end Multi-instrument Timbre Transfer with Diffusion](https://mlsanity.com/paper/2409.15321/) (2024)
- [Music Sentiment Transfer](https://mlsanity.com/paper/2110.05765/) (2021)
- [PaCaNet: A Study on CycleGAN with Transfer Learning for Diversifying Fused Chinese Painting and Calligraphy](https://mlsanity.com/paper/2301.13082/) (2023)
- [Evaluating Co-Creativity using Total Information Flow](https://mlsanity.com/paper/2402.06810/) (2024)
- [Etude: Piano Cover Generation with a Three-Stage Approach -- Extract, strucTUralize, and DEcode](https://mlsanity.com/paper/2509.16522/) (2025)
- [Music Transcription with (Almost) No Supervision](https://mlsanity.com/paper/2605.24193/) (2026)
- [Actions Speak Louder than Listening: Evaluating Music Style Transfer based on Editing Experience](https://mlsanity.com/paper/2110.12855/) (2021)
- [Can Machines Generate Personalized Music? A Hybrid Favorite-aware Method for User Preference Music Transfer](https://mlsanity.com/paper/2201.08526/) (2022)
- [Music Style Transfer With Diffusion Model](https://mlsanity.com/paper/2404.14771/) (2024)
- [Tune It Up: Music Genre Transfer and Prediction](https://mlsanity.com/paper/2503.22008/) (2025)
- [ORGAN: Object-Centric Representation Learning using Cycle Consistent Generative Adversarial Networks](https://mlsanity.com/paper/2603.02063/) (2026)
- [Remix the Timbre: Diffusion-Based Style Transfer Across Polyphonic Stems](https://mlsanity.com/paper/2605.09259/) (2026)
- [Musical Attention Transformer: Music Generation Using a Music-Specific Attention Model](https://mlsanity.com/paper/2605.21081/) (2026)

## Abstract (arXiv)

Deep generative models such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) have recently been applied to style and domain transfer for images, and in the case of VAEs, music. GAN-based models employing several generators and some form of cycle consistency loss have been among the most successful for image domain transfer. In this paper we apply such a model to symbolic music and show the feasibility of our approach for music genre transfer. Evaluations using separate genre classifiers show that the style transfer works well. In order to improve the fidelity of the transformed music, we add additional discriminators that cause the generators to keep the structure of the original music mostly intact, while still achieving strong genre transfer. Visual and audible results further show the potential of our approach. To the best of our knowledge, this paper represents the first application of GANs to symbolic music domain transfer.