  Audio quality assessment is critical for assessing the perceptual realism of
sounds. However, the time and expense of obtaining ''gold standard'' human
judgments limit the availability of such data. For AR&VR, good perceived sound
quality and localizability of sources are among the key elements to ensure
complete immersion of the user. Our work introduces SAQAM which uses a
multi-task learning framework to assess listening quality (LQ) and
spatialization quality (SQ) between any given pair of binaural signals without
using any subjective data. We model LQ by training on a simulated dataset of
triplet human judgments, and SQ by utilizing activation-level distances from
networks trained for direction of arrival (DOA) estimation. We show that SAQAM
correlates well with human responses across four diverse datasets. Since it is
a deep network, the metric is differentiable, making it suitable as a loss
function for other tasks. For example, simply replacing an existing loss with
our metric yields improvement in a speech-enhancement network.
