  Learning a mapping between two unrelated domains-such as image and audio,
without any supervision is a challenging task. In this work, we propose a
distance-preserving generative adversarial model to translate images of human
faces into an audio domain. The audio domain is defined by a collection of
musical note sounds recorded by 10 different instrument families (NSynth
\cite{nsynth2017}) and a distance metric where the instrument family class
information is incorporated together with a mel-frequency cepstral coefficients
(MFCCs) feature. To enforce distance-preservation, a loss term that penalizes
difference between pairwise distances of the faces and the translated audio
samples is used. Further, we discover that the distance preservation constraint
in the generative adversarial model leads to reduced diversity in the
translated audio samples, and propose the use of an auxiliary discriminator to
enhance the diversity of the translations while using the distance preservation
constraint. We also provide a visual demonstration of the results and numerical
analysis of the fidelity of the translations. A video demo of our proposed
model's learned translation is available in
https://www.dropbox.com/s/the176w9obq8465/face_to_musical_note.mov?dl=0.
