  The motivation of our research is to explore the possibilities of automatic
sound-to-image (S2I) translation for enabling a human receiver to visually
infer the occurrence of sound related events. We expect the computer to
'imagine' the scene from the captured sound, generating original images that
picture the sound emitting source. Previous studies on similar topics opted for
simplified approaches using data with low content diversity and/or sound class
supervision. Differently, we propose to perform unsupervised S2I translation
using thousands of distinct and unknown scenes, with slightly pre-cleaned data,
just enough to guarantee aural-visual semantic coherence. To that end, we
employ conditional generative adversarial networks (GANs) with a deep densely
connected generator. Additionally, we present a solution using informativity
classifiers to perform quantitative evaluation of the generated images. This
enabled us to analyze the influence of network bottleneck variation over the
translation, observing a potential trade-off between informativity and pixel
space convergence. Despite the complexity of the specified S2I translation
task, we were able to generalize the model enough to obtain more than 14%, in
average, of interpretable and semantically coherent images translated from
unknown sounds.
