  Automatic coded audio quality assessment is an important task whose progress
is hampered by the scarcity of human annotations, poor generalization to unseen
codecs, bitrates, content-types, and a lack of flexibility of existing
approaches. One of the typical human-perception-related metrics, ViSQOL v3
(ViV3), has been proven to provide a high correlation to the quality scores
rated by humans. In this study, we take steps to tackle problems of predicting
coded audio quality by completely utilizing programmatically generated data
that is informed with expert domain knowledge. We propose a learnable neural
network, entitled InSE-NET, with a backbone of Inception and
Squeeze-and-Excitation modules to assess the perceived quality of coded audio
at a 48kHz sample rate. We demonstrate that synthetic data augmentation is
capable of enhancing the prediction. Our proposed method is intrusive, i.e. it
requires Gammatone spectrograms of unencoded reference signals. Besides a
comparable performance to ViV3, our approach provides a more robust prediction
towards higher bitrates.
