Feature vectors were standardised prior to model training, i.e. they were scaled according to the mean and variance of each feature in the training data. For the folded cases, where the training data was different for each fold, the scaling was fold-specific. Scaling transformed each feature to have zero mean and unit variance in the training dataset. The validation and test datasets were scaled according to the scaling parameters computed on their respective training sets. The same scaling strategy (feature-wise scaling) was also applied in the case of spectrograms (config 'G'), although their features are organised in arrays rather than vectors.