Deep Learning Restoration of Signals with Additive and Convolution Noise

Localization, tracking, and classification on land requires precise isolation of the target signal amidst a sea of environmental noise and reflections from terrain and buildings. In this work, we use machine learning (ML), specifically generative-adversarial networks (GANs), to remove a range of synthetic additive noise and convolutional reverberations from a well-defined subset of sounds, namely clips of English speech. Unlike simple denoising autoencoders, GANs attempt to generate realistic solutions, suppressing the production of unresolved/blurry mixed inferences. We demonstrate that our models can dynamically resolve varying amounts of noise and convolution, which will be important in the field, where the amount and type of signal degradation will generally be unknown. While we focus only on speech here, ML-based deconvolution can also be applied to the restoration of images, radar, radio, and acoustic and seismic sensing distorted by weather, interference, and reflections.