# Real-time noise cancellation with Deep Learning

arXiv: https://arxiv.org/abs/2011.03466 · HTML: https://mlsanity.com/paper/2011.03466/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach](https://mlsanity.com/paper/2403.17181/) (2024)
- [Effects of Dataset Sampling Rate for Noise Cancellation through Deep Learning](https://mlsanity.com/paper/2405.20884/) (2024)

## Abstract (arXiv)

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.