  We explore the recently proposed explainable acoustic neural embedding~(XANE)
system that models the background acoustics of a speech signal in a
non-intrusive manner. The XANE embeddings are used to estimate specific
parameters related to the background acoustic properties of the signal which
allows the embeddings to be explainable in terms of those parameters. We
perform ablation studies on the XANE system and show that estimating all
acoustic parameters jointly has an overall positive effect. Furthermore, we
illustrate the value of XANE embeddings by performing clustering experiments on
unseen test data and show that the proposed embeddings achieve a mean F1 score
of 92\% for three different tasks, outperforming significantly the WavLM based
signal embeddings and are complimentary to speaker embeddings.
