  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.
