CNN architecture scheme. Input data form an example of a composite spectrum according to the three normalizations, i.e., by the maximum (top), hyperbolic tangent (center), and polynomial (bottom). Filters are applied to the data to convolve the information and produce features maps. This operation is done for each of the convolutional layers. Dense layers then combine the extracted features and learn how to label the spectra depending on the provided target. The output layer is composed of one neuron per class giving a score between 0 and 1, independent of each other.