A convolution neural network based machine learning approach for ultrasonic noise suppression with minimal distortion

In this paper we present a novel machine learning approach for noise suppression in the signal generated by automotive industrial grade active ultrasonic sensors. A convolutional neural network (CNN) based machine learning approach is presented. State of art noise suppression methods are also discussed and used as benchmark against the proposed machine learning approach. The results of numerous simulated scenarios as well as actual sensor measurement campaigns are presented and discussed. Several metrics are derived to quantify the quality of the signal and give an indication of the performance of the different approaches of noise suppression. These derived metrics assess the performance of the different approaches in terms of amount of noise suppressed and amount of distortion introduced to the signal of interest.