Interpretability of Learning in a Signal Processing System

This paper presents a software package that allows us to generate algorithms for the automatic classification of signals. The software package includes an algorithm that converts records of continuous signals into vector form and a set of machine learning methods, as well as data mining tools aimed at achieving transparency and interpretability of learning. This approach is based on the presentation of differences between the compared classes as a set of relatively simple, statistically significant and interpretable effects, which are graphically represented on two-dimensional diagrams. The performance of the method is illustrated on the problem of assessing the state of a hive by sound signals. The software package can be used in solving applied problems of automatic diagnostics and data analysis.