# Can Machines Generate Personalized Music? A Hybrid Favorite-aware Method for User Preference Music Transfer

arXiv: https://arxiv.org/abs/2201.08526 · HTML: https://mlsanity.com/paper/2201.08526/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 48 references that are in the map.
- First posted on arXiv 21 Jan 2022.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)
- [Neural Style Transfer: A Review](https://mlsanity.com/paper/1705.04058/) (2017)
- [Arbitrary Style Transfer with Style-Attentional Networks](https://mlsanity.com/paper/1812.02342/) (2018)
- [Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning](https://mlsanity.com/paper/1905.06088/) (2019)
- [Logic Tensor Networks for Semantic Image Interpretation](https://mlsanity.com/paper/1705.08968/) (2017)
- [PopMAG: Pop Music Accompaniment Generation](https://mlsanity.com/paper/2008.07703/) (2020)
- [One-Shot Object Detection with Co-Attention and Co-Excitation](https://mlsanity.com/paper/1911.12529/) (2019)
- [The More You Know: Using Knowledge Graphs for Image Classification](https://mlsanity.com/paper/1612.04844/) (2016)
- [LakhNES: Improving multi-instrumental music generation with cross-domain pre-training](https://mlsanity.com/paper/1907.04868/) (2019)
- [Encoding Musical Style with Transformer Autoencoders](https://mlsanity.com/paper/1912.05537/) (2019)
- [MIDI-VAE: Modeling Dynamics and Instrumentation of Music with Applications to Style Transfer](https://mlsanity.com/paper/1809.07600/) (2018)
- [TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer](https://mlsanity.com/paper/1811.09620/) (2018)
- [Deep Music Analogy Via Latent Representation Disentanglement](https://mlsanity.com/paper/1906.03626/) (2019)
- [Symbolic Music Genre Transfer with CycleGAN](https://mlsanity.com/paper/1809.07575/) (2018)
- [A Universal Music Translation Network](https://mlsanity.com/paper/1805.07848/) (2018)
- [A Bi-directional Transformer for Musical Chord Recognition](https://mlsanity.com/paper/1907.02698/) (2019)
- [Music Style Transfer: A Position Paper](https://mlsanity.com/paper/1803.06841/) (2018)
- [MelGAN-VC: Voice Conversion and Audio Style Transfer on arbitrarily long samples using Spectrograms](https://mlsanity.com/paper/1910.03713/) (2019)
- [Play as You Like: Timbre-enhanced Multi-modal Music Style Transfer](https://mlsanity.com/paper/1811.12214/) (2018)
- [Musical Composition Style Transfer via Disentangled Timbre Representations](https://mlsanity.com/paper/1905.13567/) (2019)
- [Inspecting and Interacting with Meaningful Music Representations using VAE](https://mlsanity.com/paper/1904.08842/) (2019)
- [Neural Translation of Musical Style](https://mlsanity.com/paper/1708.03535/) (2017)
- [Incorporating Music Knowledge in Continual Dataset Augmentation for Music Generation](https://mlsanity.com/paper/2006.13331/) (2020)

## Abstract (arXiv)

User preference music transfer (UPMT) is a new problem in music style transfer that can be applied to many scenarios but remains understudied.