Studies on 1D Electronic Noise Filtering Using an Autoencoder

Abstract

Autoencoders are neural networks that have applications in denoising processes. Their use is widely reported in imaging (2D), though 1D series can also benefit from this function. Here, three canonical waveforms are used to train a neural network and achieve a signal-to-noise reduction with curves whose noise energy is above that of the signals. A real-world test is carried out with the same autoencoder subjected to a set of time series corrupted by noise generated by a Zener diode, biased on the avalanche region. Results showed that, observing some guidelines, the autoencoder can indeed denoise 1D waveforms usually observed in electronics, particularly square waves found in digital circuits. Results showed an average of 2.8 dB in the signal-to-noise ratio for square and triangular waveforms.

Keywords

• noise filtering
• denoise
• deep convolutional autoencoder
• noise generator