  Abstract This project presents a system of neural networks to translate
between images and melodies. Autoencoders compress the information in samples
to abstract representation. A translation network learns a set of
correspondences between musical and visual concepts from repeated joint
exposure. The resulting "artificial synesthete" generates simple melodies
inspired by images, and images from music. These are novel interpretation (not
transposed data), expressing the machine' perception and understanding.
Observing the work, one explores the machine's perception and thus, by
contrast, one's own.
