  Audio features have been proven useful for increasing the performance of
automated topic segmentation systems. This study explores the novel task of
using audio embeddings for automated, topically coherent segmentation of radio
shows. We created three different audio embedding generators using multi-class
classification tasks on three datasets from different domains. We evaluate
topic segmentation performance of the audio embeddings and compare it against a
text-only baseline. We find that a set-up including audio embeddings generated
through a non-speech sound event classification task significantly outperforms
our text-only baseline by 32.3% in F1-measure. In addition, we find that
different classification tasks yield audio embeddings that vary in segmentation
performance.
