Signal classification with machine learning

Abstract

This paper investigates and evaluates several Machine Learning techniques for the proper identification and classification of analytical signals. Signals having different “shapes” and periods were defined analytically to have pre-determined class associations. Supervised Machine Learning techniques were then investigated to evaluate the Machine Learning methodology’s ability to properly classify the analytical signals based on characteristics of interest.