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arXiv · 2011.03466

Real-time noise cancellation with Deep Learning

Bernd Porr·Sama Daryanavard·Lucía Muñoz Bohollo·Henry Cowan·Ravinder Dahiya
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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.

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Bernd Porr, Sama Daryanavard, Lucía Muñoz Bohollo, Henry Cowan, Ravinder Dahiya. 2020-11-06. Real-time noise cancellation with Deep Learning. https://arxiv.org/abs/2011.03466

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