A computer-implemented spectroscopic method for improved NMR signal detection, comprising: receiving NMR signal data produced by a sample over time in response to an excitation pulse; selecting a predefined system function (S, 121) for application to the NMR signal data for systematic variation of signal properties wherein the system function has a different influence on NMR signal components than on noise components of the NMR signal data, the system function having a variation parameter to control the systematic variation; providing a plurality of differing variation parameter values wherein the provided variation parameter values are adapted for influencing, in the frequency-domain, NMR signals with a signal intensity within a given interval around the mean signal intensity of the NMR signal data, and NMR signals with a signal width within a given interval around the mean signal width of the NMR signal data; generating for each variation parameter value (VP-1 to VP-n) a corresponding intermediate data set (102-1 to 102-n) by applying the system function with the respective variation parameter value (VP-1 to VP-n) to the NMR signal data; generating from each intermediate data set (102-1 to 102-n) a respective base value centered spectrum (103-1 to 103-n) in the frequency-domain wherein the respective base value centered spectra eliminate offsets from the intermediate data sets; detecting signal intervals by extracting from the base value centered spectra (103-1 to 103-n) for each frequency point variations induced by the system function and identifying frequency intervals with induced variations exceeding respective weighted noise values as signal intervals; characterized in that generating base value centered spectra further comprises: computing for each frequency point of the intermediate data sets (102-1 to 102-n) in the frequency-domain an ensemble base value based on the respective values at that frequency point of all intermediate data sets; subtracting for each frequency the computed ensemble base values from the respective values of the intermediate data sets resulting in a corresponding base value centered spectrum (103-1 to 103-n) for each intermediate data set (102-1 to 102-n). receiving NMR signal data produced by a sample over time in response to an excitation pulse; selecting a predefined system function (S, 121) for application to the NMR signal data for systematic variation of signal properties wherein the system function has a different influence on NMR signal components than on noise components of the NMR signal data, the system function having a variation parameter to control the systematic variation; providing a plurality of differing variation parameter values wherein the provided variation parameter values are adapted for influencing, in the frequency-domain, NMR signals with a signal intensity within a given interval around the mean signal intensity of the NMR signal data, and NMR signals with a signal width within a given interval around the mean signal width of the NMR signal data; generating for each variation parameter value (VP-1 to VP-n) a corresponding intermediate data set (102-1 to 102-n) by applying the system function with the respective variation parameter value (VP-1 to VP-n) to the NMR signal data; generating from each intermediate data set (102-1 to 102-n) a respective base value centered spectrum (103-1 to 103-n) in the frequency-domain wherein the respective base value centered spectra eliminate offsets from the intermediate data sets; detecting signal intervals by extracting from the base value centered spectra (103-1 to 103-n) for each frequency point variations induced by the system function and identifying frequency intervals with induced variations exceeding respective weighted noise values as signal intervals; characterized in that generating base value centered spectra further comprises: computing for each frequency point of the intermediate data sets (102-1 to 102-n) in the frequency-domain an ensemble base value based on the respective values at that frequency point of all intermediate data sets; subtracting for each frequency the computed ensemble base values from the respective values of the intermediate data sets resulting in a corresponding base value centered spectrum (103-1 to 103-n) for each intermediate data set (102-1 to 102-n). computing for each frequency point of the intermediate data sets (102-1 to 102-n) in the frequency-domain an ensemble base value based on the respective values at that frequency point of all intermediate data sets; subtracting for each frequency the computed ensemble base values from the respective values of the intermediate data sets resulting in a corresponding base value centered spectrum (103-1 to 103-n) for each intermediate data set (102-1 to 102-n).