A method for training a deep neural network classifier for use in detecting impairments in a communication network, the communication network comprising a plurality of communication lines, the method comprising: a) obtaining a measured loop frequency response for the respective communication lines comprised in the communication network, wherein, the loop frequency response is measured by a modem over the respective communication line; b) extracting a modem parasitic effect for the respective communication lines from the measured loop frequency response of the respective communication line by determining an ideal loop frequency response for the respective communication lines based on the measured loop frequency response of the respective communication line and by determining the modem parasitic effect for the respective communication lines as the difference between the measured loop frequency response and the ideal loop frequency response of the respective communication line; c) clustering the modem parasitic effect of the respective communication lines into a first number M of groups and determining a modem signature for respective ones of the first number M of groups; d) obtaining an original training data set comprising a second number N of loop frequency responses corresponding to a first set of communication lines and associated information on the presence of impairment on the first set of communication lines; e) determining an augmented training data set comprising a third number of loop frequency responses and associated information on the presence of impairment based on the original training data set and the modem signature for respective ones of the first number of groups, wherein, the third number is not greater than N*M by subtracting respectively from respective ones of the second number N of loop frequency responses its corresponding modem signature, obtaining an intermediary loop frequency response for the respective ones of the second number N of loop frequency responses respectively, and by adding the first number M of modem signatures respectively onto the respective intermediary loop frequency response; f) training the deep neural network classifier using the augmented training data set. a) obtaining a measured loop frequency response for the respective communication lines comprised in the communication network, wherein, the loop frequency response is measured by a modem over the respective communication line; b) extracting a modem parasitic effect for the respective communication lines from the measured loop frequency response of the respective communication line by determining an ideal loop frequency response for the respective communication lines based on the measured loop frequency response of the respective communication line and by determining the modem parasitic effect for the respective communication lines as the difference between the measured loop frequency response and the ideal loop frequency response of the respective communication line; c) clustering the modem parasitic effect of the respective communication lines into a first number M of groups and determining a modem signature for respective ones of the first number M of groups; d) obtaining an original training data set comprising a second number N of loop frequency responses corresponding to a first set of communication lines and associated information on the presence of impairment on the first set of communication lines; e) determining an augmented training data set comprising a third number of loop frequency responses and associated information on the presence of impairment based on the original training data set and the modem signature for respective ones of the first number of groups, wherein, the third number is not greater than N*M by subtracting respectively from respective ones of the second number N of loop frequency responses its corresponding modem signature, obtaining an intermediary loop frequency response for the respective ones of the second number N of loop frequency responses respectively, and by adding the first number M of modem signatures respectively onto the respective intermediary loop frequency response; f) training the deep neural network classifier using the augmented training data set.