A method for supervised training of a machine learning model, MLM, to enhance a degraded audio signal by calculating gains to be applied to frequency bands of the degraded audio signal, the method comprising the steps of: receiving a degraded audio signal and a clean audio signal for training of the MLM; extracting (S104, S106) a first set of features from the received degraded audio signal, and a second set of features from the received clean audio signal, each feature corresponding to a frequency band of the respective received audio signals; comparing each feature of the first set of features to a corresponding feature of the second set of features to derive a set of gains, each gain corresponding to a respective feature among the first set of features, and used as ground truth when training the MLM; using the first set of features and the derived set of gains as a training set for training the MLM; wherein the method further comprises at least one of: a pre-processing step performed prior to deriving the set of gains, wherein the pre-processing step comprises adjusting the frequency energy distribution of the first and/or the second set of features such that a difference between the frequency energy distribution of the first set of features and the frequency energy distribution of the second set of features is reduced, and defining a loss function of the MLM which is configured to punish a predicted gain being lower than the ground truth gain more than a predicted gain being higher than the ground truth gain. receiving a degraded audio signal and a clean audio signal for training of the MLM; extracting (S104, S106) a first set of features from the received degraded audio signal, and a second set of features from the received clean audio signal, each feature corresponding to a frequency band of the respective received audio signals; comparing each feature of the first set of features to a corresponding feature of the second set of features to derive a set of gains, each gain corresponding to a respective feature among the first set of features, and used as ground truth when training the MLM; using the first set of features and the derived set of gains as a training set for training the MLM; wherein the method further comprises at least one of: a pre-processing step performed prior to deriving the set of gains, wherein the pre-processing step comprises adjusting the frequency energy distribution of the first and/or the second set of features such that a difference between the frequency energy distribution of the first set of features and the frequency energy distribution of the second set of features is reduced, and defining a loss function of the MLM which is configured to punish a predicted gain being lower than the ground truth gain more than a predicted gain being higher than the ground truth gain. a pre-processing step performed prior to deriving the set of gains, wherein the pre-processing step comprises adjusting the frequency energy distribution of the first and/or the second set of features such that a difference between the frequency energy distribution of the first set of features and the frequency energy distribution of the second set of features is reduced, and defining a loss function of the MLM which is configured to punish a predicted gain being lower than the ground truth gain more than a predicted gain being higher than the ground truth gain.