  We present a novel method for extracting neural embeddings that model the
background acoustics of a speech signal. The extracted embeddings are used to
estimate specific parameters related to the background acoustic properties of
the signal in a non-intrusive manner, which allows the embeddings to be
explainable in terms of those parameters. We illustrate the value of these
embeddings by performing clustering experiments on unseen test data and show
that the proposed embeddings achieve a mean F1 score of 95.2\% for three
different tasks, outperforming significantly the WavLM based signal embeddings.
We also show that the proposed method can explain the embeddings by estimating
14 acoustic parameters characterizing the background acoustics, including
reverberation and noise levels, overlapped speech detection, CODEC type
detection and noise type detection with high accuracy and a real-time factor 17
times lower than an external baseline method.
