Deep learning restoration of signals with additive and convolution noise

Michael S. Lee

doi:10.1117/12.2585170

12 April 2021 Deep learning restoration of signals with additive and convolution noise

Michael S. Lee

Proceedings Volume 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III; 1174617 (2021) https://doi.org/10.1117/12.2585170
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Localization, tracking, and classification requires precise recovery 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 English speech. Unlike simple denoising autoencoders, GANs attempt to generate realistic solutions, suppressing the production of unresolved (i.e., 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.

Conference Presentation

Citation Download Citation

Michael S. Lee "Deep learning restoration of signals with additive and convolution noise", Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 1174617 (12 April 2021); https://doi.org/10.1117/12.2585170

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available