  Advanced Generative Adversarial Networks (GANs) are remarkable in generating
intelligible audio from a random latent vector. In this paper, we examine the
task of recovering the latent vector of both synthesized and real audio.
Previous works recovered latent vectors of given audio through an auto-encoder
inspired technique that trains an encoder network either in parallel with the
GAN or after the generator is trained. With our approach, we train a deep
residual neural network architecture to project audio synthesized by WaveGAN
into the corresponding latent space with near identical reconstruction
performance. To accommodate for the lack of an original latent vector for real
audio, we optimize the residual network on the perceptual loss between the real
audio samples and the reconstructed audio of the predicted latent vectors. In
the case of synthesized audio, the Mean Squared Error (MSE) between the ground
truth and recovered latent vector is minimized as well. We further investigated
the audio reconstruction performance when several gradient optimization steps
are applied to the predicted latent vector. Through our deep neural network
based method of training on real and synthesized audio, we are able to predict
a latent vector that corresponds to a reasonable reconstruction of real audio.
Even though we evaluated our method on WaveGAN, our proposed method is
universal and can be applied to any other GANs.
