# Music Style Transfer: A Position Paper

arXiv: https://arxiv.org/abs/1803.06841 · HTML: https://mlsanity.com/paper/1803.06841/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2018.

## Built on by (most cited first, 16 of 16)

- [MARBLE: Music Audio Representation Benchmark for Universal Evaluation](https://mlsanity.com/paper/2306.10548/) (2023)
- [Polyffusion: A Diffusion Model for Polyphonic Score Generation with Internal and External Controls](https://mlsanity.com/paper/2307.10304/) (2023)
- [MusicMagus: Zero-Shot Text-to-Music Editing via Diffusion Models](https://mlsanity.com/paper/2402.06178/) (2024)
- [Music Style Transfer with Time-Varying Inversion of Diffusion Models](https://mlsanity.com/paper/2402.13763/) (2024)
- [Reconstructing Human Expressiveness in Piano Performances with a Transformer Network](https://mlsanity.com/paper/2306.06040/) (2023)
- [Audio-to-symbolic Arrangement via Cross-modal Music Representation Learning](https://mlsanity.com/paper/2112.15110/) (2021)
- [Personalized Popular Music Generation Using Imitation and Structure](https://mlsanity.com/paper/2105.04709/) (2021)
- [A Survey on Artificial Intelligence for Music Generation: Agents, Domains and Perspectives](https://mlsanity.com/paper/2210.13944/) (2022)
- [Real-time Timbre Remapping with Differentiable DSP](https://mlsanity.com/paper/2407.04547/) (2024)
- [ImprovNet -- Generating Controllable Musical Improvisations with Iterative Corruption Refinement](https://mlsanity.com/paper/2502.04522/) (2025)
- [JAZZVAR: A Dataset of Variations found within Solo Piano Performances of Jazz Standards for Music Overpainting](https://mlsanity.com/paper/2307.09670/) (2023)
- [Repurposing Image Diffusion Models for Training-Free Music Style Transfer on Mel-spectrograms](https://mlsanity.com/paper/2411.15913/) (2024)
- [Revisiting Your Memory: Reconstruction of Affect-Contextualized Memory via EEG-guided Audiovisual Generation](https://mlsanity.com/paper/2412.05296/) (2024)
- [Can Machines Generate Personalized Music? A Hybrid Favorite-aware Method for User Preference Music Transfer](https://mlsanity.com/paper/2201.08526/) (2022)
- [Device-Guided Music Transfer](https://mlsanity.com/paper/2511.17136/) (2025)
- [Latent Fourier Transform](https://mlsanity.com/paper/2604.17986/) (2026)

## Abstract (arXiv)

Led by the success of neural style transfer on visual arts, there has been a rising trend very recently in the effort of music style transfer. However, "music style" is not yet a well-defined concept from a scientific point of view. The difficulty lies in the intrinsic multi-level and multi-modal character of music representation (which is very different from image representation). As a result, depending on their interpretation of "music style", current studies under the category of "music style transfer", are actually solving completely different problems that belong to a variety of sub-fields of Computer Music. Also, a vanilla end-to-end approach, which aims at dealing with all levels of music representation at once by directly adopting the method of image style transfer, leads to poor results. Thus, we vitally propose a more scientifically-viable definition of music style transfer by breaking it down into precise concepts of timbre style transfer, performance style transfer and composition style transfer, as well as to connect different aspects of music style transfer with existing well-established sub-fields of computer music studies. In addition, we discuss the current limitations of music style modeling and its future directions by drawing spirit from some deep generative models, especially the ones using unsupervised learning and disentanglement techniques.