# Device-Guided Music Transfer

arXiv: https://arxiv.org/abs/2511.17136 · HTML: https://mlsanity.com/paper/2511.17136/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 21 Nov 2025.

## Builds on (most cited first, 9 of 9)

- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [Hybrid Transformers for Music Source Separation](https://mlsanity.com/paper/2211.08553/) (2022)
- [MIDI-VAE: Modeling Dynamics and Instrumentation of Music with Applications to Style Transfer](https://mlsanity.com/paper/1809.07600/) (2018)
- [Music Style Transfer: A Position Paper](https://mlsanity.com/paper/1803.06841/) (2018)
- [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)
- [Music Style Transfer with Time-Varying Inversion of Diffusion Models](https://mlsanity.com/paper/2402.13763/) (2024)
- [MicAugment: One-shot Microphone Style Transfer](https://mlsanity.com/paper/2010.09658/) (2020)
- [Deep Optimization of Parametric IIR Filters for Audio Equalization](https://mlsanity.com/paper/2110.02077/) (2021)

## Abstract (arXiv)

Device-guided music transfer adapts playback across unseen devices for users who lack them. Existing methods mainly focus on modifying the timbre, rhythm, harmony, or instrumentation to mimic genres or artists, overlooking the diverse hardware properties of the playback device (i.e., speaker). Therefore, we propose DeMT, which processes a speaker's frequency response curve as a line graph using a vision-language model to extract device embeddings. These embeddings then condition a hybrid transformer via feature-wise linear modulation. Fine-tuned on a self-collected dataset, DeMT enables effective speaker-style transfer and robust few-shot adaptation for unseen devices, supporting applications like device-style augmentation and quality enhancement.