  How does audio describe the world around us? In this paper, we propose a
method for generating an image of a scene from sound. Our method addresses the
challenges of dealing with the large gaps that often exist between sight and
sound. We design a model that works by scheduling the learning procedure of
each model component to associate audio-visual modalities despite their
information gaps. The key idea is to enrich the audio features with visual
information by learning to align audio to visual latent space. We translate the
input audio to visual features, then use a pre-trained generator to produce an
image. To further improve the quality of our generated images, we use sound
source localization to select the audio-visual pairs that have strong
cross-modal correlations. We obtain substantially better results on the VEGAS
and VGGSound datasets than prior approaches. We also show that we can control
our model's predictions by applying simple manipulations to the input waveform,
or to the latent space.
