  This paper presents a comparison of several Convolutional Neural Network
(CNN) models for extracting target signals in highly noisy measurement
conditions. Four CNN architectures were investigated. The first comprises six
consecutive convolutional blocks while the second employs a U-Net structure.
The third architecture introduces a new model inspired by the principles of
wavelet transform. It consists of three CNN blocks with varied kernel sizes
branching from the input layer before merging into consecutive concatenation
and dense layers. The fourth is a Multilevel Wavelet Convolutional Neural
Network (MWCNN), resembling U-net but the upsampling and downsampling are
replaced by Discrete Wavelet transform and its inverse respectively. To
evaluate these architectures, synthetic data were generated using pulse trains
corrupted with various degrees of Gaussian noise to simulate measurement
conditions with signal-to-noise ratios (SNR) as low as -20 dB. The methodology
encompassed the generation and processing of signals with varied parameters:
period (5-25 ms), duty cycle (0.1-0.5), and amplitude (1-150 mV). Subsequently,
the machine learning models were trained, validated and tested. Finally, the
output is processed to recover the signal amplitude before standardisation. The
modified MWCNN model demonstrated superior performance achieving a median of
25.9 dB with a mean Root Mean Square Error (RMSE) that decreases with the
amplitude of the signal reaching an average RMSE of 0.000128 mV for 1 mV
signals. However, for this architecture, the output SNR drops by a factor of
1.23 when the input SNR decreases by 1 dB. All of the architectures exhibited
consistency when testing output SNR for different signal amplitudes, periods
and duty cycles. These findings indicate that CNN architectures can be used to
denoise signals with SNR as low as -20 dB[...]
