Bandwidth Extension on Raw Audio via Generative Adversarial Networks
This work addresses audio bandwidth extension for applications like audio enhancement, but it is incremental as it adapts existing GAN techniques from images to audio.
The paper tackles audio super-resolution by developing a GAN-based method with a convolutional neural network architecture and an autoencoder-based loss, achieving significant improvements in objective and perceptual quality over previous works.
Neural network-based methods have recently demonstrated state-of-the-art results on image synthesis and super-resolution tasks, in particular by using variants of generative adversarial networks (GANs) with supervised feature losses. Nevertheless, previous feature loss formulations rely on the availability of large auxiliary classifier networks, and labeled datasets that enable such classifiers to be trained. Furthermore, there has been comparatively little work to explore the applicability of GAN-based methods to domains other than images and video. In this work we explore a GAN-based method for audio processing, and develop a convolutional neural network architecture to perform audio super-resolution. In addition to several new architectural building blocks for audio processing, a key component of our approach is the use of an autoencoder-based loss that enables training in the GAN framework, with feature losses derived from unlabeled data. We explore the impact of our architectural choices, and demonstrate significant improvements over previous works in terms of both objective and perceptual quality.