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We show how a neural network can be trained on individual intrusive listeningtest scores to predict a distribution of scores for each pair of reference andcoded input stereo or binaural signals. We nickname this method the GenerativeMachine Listener (GML), as it is capable of generating an arbitrary amount ofsimulated listening test data. Compared to a baseline system using regressionover mean scores, we observe lower outlier ratios (OR) for the mean scorepredictions, and obtain easy access to the prediction of confidence intervals(CI). The introduction of data augmentation techniques from the image domainresults in a significant increase in CI prediction accuracy as well as Pearsonand Spearman rank correlation of mean scores.

Generative Machine Listener