  Inspired by sophisticated echolocation abilities found in nature, we train a
generative adversarial network to predict plausible depth maps and grayscale
layouts from sound. To achieve this, our sound-to-vision model processes
binaural echo-returns from chirping sounds. We build upon previous work with
BatVision that consists of a sound-to-vision model and a self-collected dataset
using our mobile robot and low-cost hardware. We improve on the previous model
by introducing several changes to the model, which leads to a better depth and
grayscale estimation, and increased perceptual quality. Rather than using raw
binaural waveforms as input, we generate generalized cross-correlation (GCC)
features and use these as input instead. In addition, we change the model
generator and base it on residual learning and use spectral normalization in
the discriminator. We compare and present both quantitative and qualitative
improvements over our previous BatVision model.
