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This project presents a system of neural networks to translatebetween images and melodies. Autoencoders compress the information in samplesto abstract representation. A translation network learns a set ofcorrespondences between musical and visual concepts from repeated jointexposure. The resulting "artificial synesthete" generates simple melodiesinspired by images, and images from music. These are novel interpretation (nottransposed data), expressing the machine' perception and understanding.Observing the work, one explores the machine's perception and thus, bycontrast, one's own.

The artificial synesthete: Image-melody translations with variational autoencoders