Using Deep Learning to Reconstruct High-Resolution Audio

Audio super-resolution aims to reconstruct a high-resolution audio waveform given a lower-resolution waveform as input. There are several potential applications for this type of upsampling in such areas as streaming audio and audio restoration. One traditional solution is to use a database of audio clips to fill in the missing frequencies in the downsampled waveform using a similarity metric (see this and this paper). Inspired by the successful applications of deep learning to image super-resolution, there is recent interest in using deep neural networks to accomplish this upsampling on raw audio waveforms. After prototyping several methods, I focused on implementing and customizing recently published research from the 2017 International Conference on Learning Representations (ICLR).