# Upmixing via style transfer: a variational autoencoder for disentangling spatial images and musical content

arXiv: https://arxiv.org/abs/2203.12053 · HTML: https://mlsanity.com/paper/2203.12053/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2022.

## Built on by (most cited first, 1 of 1)

- [Learning to Upsample and Upmix Audio in the Latent Domain](https://mlsanity.com/paper/2506.00681/) (2025)

## Abstract (arXiv)

In the stereo-to-multichannel upmixing problem for music, one of the main tasks is to set the directionality of the instrument sources in the multichannel rendering results. In this paper, we propose a modified variational autoencoder model that learns a latent space to describe the spatial images in multichannel music. We seek to disentangle the spatial images and music content, so the learned latent variables are invariant to the music. At test time, we use the latent variables to control the panning of sources. We propose two upmixing use cases: transferring the spatial images from one song to another and blind panning based on the generative model. We report objective and subjective evaluation results to empirically show that our model captures spatial images separately from music content and achieves transfer-based interactive panning.