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The module selects the echolyst data sets and offers a variety of preprocessing options targeted at impact-echo and ultrasonic data. The preprocessing includes signal processing in the time as well as the frequency domain, time-frequency spectrogram calculation using the Continuous Wavelet and Short-Time Fourier trans-form, filtering, etc. The data set management includes labeling of the data sets and splitting them into balanced training and test sets. It is handled interactively and supported by graphical user interfaces. 

Deep Learning architectures can be defined in user-friendly dialogs and are displayed in technical plots. Multi-Layer Perceptron Neural Networks (MLP) as well as Convolutional Neural Networks (CNN) are offered. Classification as well as regression models can be trained. The resulting learning curves state the basis for further model adjustment.

The software accesses the widely established Tensor-flow library for model generation, thus ensuring con-sistent results and compatibility with Python coding.