# Translating Visual Art into Music

arXiv: https://arxiv.org/abs/1909.01218 · HTML: https://mlsanity.com/paper/1909.01218/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2019.

## Built on by (most cited first, 2 of 2)

- [Audio-to-Image Cross-Modal Generation](https://mlsanity.com/paper/2109.13354/) (2021)
- [The artificial synesthete: Image-melody translations with variational autoencoders](https://mlsanity.com/paper/2112.02953/) (2021)

## Abstract (arXiv)

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%.