Real-time noise cancellation with Deep Learning
Abstract
Biological measurements are often contaminated with large amounts of non-stationary noise which require effective noise reduction techniques. We present a new real-time deep learning algorithm which produces adaptively a signal opposing the noise so that destructive interference occurs. As a proof of concept, we demonstrate the algorithm's performance by reducing electromyogram noise in electroencephalograms with the usage of a custom, flexible, 3D-printed, compound electrode. With this setup, an average of 4dB and a maximum of 10dB improvement of the signal-to-noise ratio of the EEG was achieved by removing wide band muscle noise. This concept has the potential to not only adaptively improve the signal-to-noise ratio of EEG but can be applied to a wide range of biological, industrial and consumer applications such as industrial sensing or noise cancelling headphones.
Cite
@article{arxiv.2011.03466,
title = {Real-time noise cancellation with Deep Learning},
author = {Bernd Porr and Sama Daryanavard and Lucía Muñoz Bohollo and Henry Cowan and Bernd Porr and Ravinder Dahiya},
journal= {arXiv preprint arXiv:2011.03466},
year = {2022}
}
Comments
21 pages, 4 figures, code available under https://doi.org/10.5281/zenodo.7100537 and EEG data available here: https://researchdata.gla.ac.uk/1258/