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Deep learning based visual to sound generation systems essentially need to bedeveloped particularly considering the synchronicity aspects of visual andaudio features with time. In this research we introduce a novel task of guidinga class conditioned generative adversarial network with the temporal visualinformation of a video input for visual to sound generation task adapting thesynchronicity traits between audio-visual modalities. Our proposed FoleyGANmodel is capable of conditioning action sequences of visual events leadingtowards generating visually aligned realistic sound tracks. We expand ourpreviously proposed Automatic Foley dataset to train with FoleyGAN and evaluateour synthesized sound through human survey that shows noteworthy (on average81\%) audio-visual synchronicity performance. Our approach also outperforms instatistical experiments compared with other baseline models and audio-visualdatasets.

FoleyGAN: Visually Guided Generative Adversarial Network-Based Synchronous Sound Generation in Silent Videos