  Automatic coded audio quality predictors are typically designed for
evaluating single channels without considering any spatial aspects. With
InSE-NET [1], we demonstrated mimicking a state-of-the-art coded audio quality
metric (ViSQOL-v3 [2]) with deep neural networks (DNN) and subsequently
improving it - completely with programmatically generated data. In this study,
we take steps towards building a DNN-based coded stereo audio quality predictor
and we propose an extension of the InSE-NET for handling stereo signals. The
design considers stereo/spatial aspects by conditioning the model with left,
right, mid, and side channels; and we name our model Stereo InSE-NET. By
transferring selected weights from the pre-trained mono InSE-NET and retraining
with both real and synthetically augmented listening tests, we demonstrate a
significant improvement of 12% and 6% of Pearson and Spearman Rank correlation
coefficient, respectively, over the latest ViSQOL-v3 [3].
