A computer-implemented method for generating a training data set for training a neural network for signal analysis in NMR spectra, comprising: obtaining a plurality of computed NMR raw spectra , each raw spectrum being associated with a different NMR active molecule having a known number of protons (#P); characterized in that the method further comprises broadening line widths of the raw spectra by convolution of each raw spectrum with one or more line shaping functions to generate a broadened spectrum as a current spectrum for each raw spectrum, wherein the broadening of line widths follows a statistical distribution over the plurality of current spectra by sampling respective broadening values for the various raw spectra from the statistical distribution; computing for each broadened spectrum its integral function to count the number of protons associated with peaks of the respective broadened spectrum; identifying signal intervals as intervals in the broadened spectrum where the integral function increases approximately by multiples of the value associated with a single proton so that the total number of counted protons matches the known number of protons (#P) of the associated molecule, the total number of protons being counted by computing an indefinite integral function for the entire broadened spectrum which is scaled such that the finite integral over the whole spectrum matches the known number of protons of the associated molecule, wherein the identified intervals are adjusted to cover at least a predefined threshold value of the corresponding known number of protons, with a signal interval being defined as a frequency interval which is centered around the peak frequency of a respective signal peak in an NMR spectrum and characterizes a part of the NMR spectrum which is associated with a nuclear magnetic resonance response of NMR active molecules in response to a radio frequency pulse; providing the current spectra with associated labels for the identified signal intervals as the training data set to the neural network. broadening line widths of the raw spectra by convolution of each raw spectrum with one or more line shaping functions to generate a broadened spectrum as a current spectrum for each raw spectrum, wherein the broadening of line widths follows a statistical distribution over the plurality of current spectra by sampling respective broadening values for the various raw spectra from the statistical distribution; computing for each broadened spectrum its integral function to count the number of protons associated with peaks of the respective broadened spectrum; identifying signal intervals as intervals in the broadened spectrum where the integral function increases approximately by multiples of the value associated with a single proton so that the total number of counted protons matches the known number of protons (#P) of the associated molecule, the total number of protons being counted by computing an indefinite integral function for the entire broadened spectrum which is scaled such that the finite integral over the whole spectrum matches the known number of protons of the associated molecule, wherein the identified intervals are adjusted to cover at least a predefined threshold value of the corresponding known number of protons, with a signal interval being defined as a frequency interval which is centered around the peak frequency of a respective signal peak in an NMR spectrum and characterizes a part of the NMR spectrum which is associated with a nuclear magnetic resonance response of NMR active molecules in response to a radio frequency pulse; providing the current spectra with associated labels for the identified signal intervals as the training data set to the neural network. <EOS>