Audio Future Block Prediction with Conditional Generative Adversarial Network

Abstract

Signal processing is a vast subfield of electrical and computer science where audio signal processing has secured a remarkable position to restore corrupted or missing audio blocks. However, generating possible future audio block from the previous audio block is still a new idea that can help to reduce both audio noise and partially missing an audio segment. In this paper, a generative adversarial network (GAN) along with a pipeline is proposed for the prediction of possible audio after an input audio sequence. The proposed model uses short-time Fourier transformation of audio to make it an image. The image is then fed to a conditional GAN to predict the output image. After that, Inverse short-time Fourier transform is then applied to that predicted image, generating the predicted audio sequence. For a small audio sequence prediction, the proposed methodology is quite fast, robust and has achieved a loss of 0.43. So it is may work well if deployed on a voice call and broadcasting applications.