  We describe a new convolutional framework for waveform evaluation, WEnets,
and build a Narrowband Audio Waveform Evaluation Network, or NAWEnet, using
this framework. NAWEnet is single-ended (or no-reference) and was trained three
separate times in order to emulate PESQ, POLQA, or STOI with testing
correlations 0.95, 0.92, and 0.95, respectively when training on only 50% of
available data and testing on 40%. Stacks of 1-D convolutional layers and
non-linear downsampling learn which features are important for quality or
intelligibility estimation. This straightforward architecture simplifies the
interpretation of its inner workings and paves the way for future
investigations into higher sample rates and accurate no-reference subjective
speech quality predictions.
