Neural Synthesis of Footsteps Sound Effects with Generative Adversarial Networks

To this day, there has not yet been an attempt at exploring the use of neural networks for the synthesis of footsteps sounds although Footsteps are among the most ubiquitous sound effects in multimedia there is substantial literature exploring neural synthesis of broadband applications. There is substantial research into understanding impulsive sounds, such as drums samples, which have some the acoustic features and developing synthesis models for footstep similarities to footsteps. One of the first attempts was in [15], where sound effects. In this paper, we present a first attempt at adopting Donahue et al. developed WaveGAN - a generative adversarial network neural synthesis for this task. We implemented two GAN-based architectures for unconditional audio synthesis. Another example of neural and compared the results with real recordings as well as synthesis of drums is [16], where the authors used a Progressive six traditional sound synthesis methods. Our architectures reached Growing GAN. Variational autoencoders [17] and U-Nets [18] realism scores as high as recorded samples, showing encouraging have also been used for the same task.