  With the rise in multimedia content over the years, more variety is observed
in the recording environments of audio. An audio processing system might
benefit when it has a module to identify the acoustic domain at its front-end.
In this paper, we demonstrate the idea of \emph{acoustic domain identification}
(ADI) for \emph{speaker diarization}. For this, we first present a detailed
study of the various domains of the third DIHARD challenge highlighting the
factors that differentiated them from each other. Our main contribution is to
develop a simple and efficient solution for ADI. In the present work, we
explore speaker embeddings for this task. Next, we integrate the ADI module
with the speaker diarization framework of the DIHARD III challenge. The
performance substantially improved over that of the baseline when the
thresholds for agglomerative hierarchical clustering were optimized according
to the respective domains. We achieved a relative improvement of more than
$5\%$ and $8\%$ in DER for core and full conditions, respectively, on Track 1
of the DIHARD III evaluation set.
