  The Synesthetic Variational Autoencoder (SynVAE) introduced in this research
is able to learn a consistent mapping between visual and auditive sensory
modalities in the absence of paired datasets. A quantitative evaluation on
MNIST as well as the Behance Artistic Media dataset (BAM) shows that SynVAE is
capable of retaining sufficient information content during the translation
while maintaining cross-modal latent space consistency. In a qualitative
evaluation trial, human evaluators were furthermore able to match musical
samples with the images which generated them with accuracies of up to 73%.
