Simple and automated supervised machine learning at Acoustic Emission data with VisualClass

VisualClass is a machine learning based software for the classification of acoustic emission signals by using features from the frequency range. For example, to specify different sources such as friction, crack formation or noise interferences. The trained classification models can be exported to classify new AE signals in real time or offline. The major features are: automatic training data-test splits automatic parameter search for model optimisation a greatly simplified workflow