Title: Towards Audio to Scene Image Synthesis using Generative Adversarial Network,
Abstract: Humans can imagine a scene from a sound. We want machines to do so by using
conditional generative adversarial networks (GANs). By applying the techniques
including spectral norm, projection discriminator and auxiliary classifier,
compared with naive conditional GAN, the model can generate images with better
quality in terms of both subjective and objective evaluations. Almost
three-fourth of people agree that our model have the ability to generate images
related to sounds. By inputting different volumes of the same sound, our model
output different scales of changes based on the volumes, showing that our model
truly knows the relationship between sounds and images to some extent.