  Deep learning based visual to sound generation systems essentially need to be
developed particularly considering the synchronicity aspects of visual and
audio features with time. In this research we introduce a novel task of guiding
a class conditioned generative adversarial network with the temporal visual
information of a video input for visual to sound generation task adapting the
synchronicity traits between audio-visual modalities. Our proposed FoleyGAN
model is capable of conditioning action sequences of visual events leading
towards generating visually aligned realistic sound tracks. We expand our
previously proposed Automatic Foley dataset to train with FoleyGAN and evaluate
our synthesized sound through human survey that shows noteworthy (on average
81\%) audio-visual synchronicity performance. Our approach also outperforms in
statistical experiments compared with other baseline models and audio-visual
datasets.
