  Devices capable of detecting and categorizing acoustic scenes have numerous
applications such as providing context-aware user experiences. In this paper,
we address the task of characterizing acoustic scenes in a workplace setting
from audio recordings collected with wearable microphones. The acoustic scenes,
tracked with Bluetooth transceivers, vary dynamically with time from the
egocentric perspective of a mobile user. Our dataset contains experience
sampled long audio recordings collected from clinical providers in a hospital,
who wore the audio badges during multiple work shifts. To handle the long
egocentric recordings, we propose a Time Delay Neural Network~(TDNN)-based
segment-level modeling. The experiments show that TDNN outperforms other models
in the acoustic scene classification task. We investigate the effect of primary
speaker's speech in determining acoustic scenes from audio badges, and provide
a comparison between performance of different models. Moreover, we explore the
relationship between the sequence of acoustic scenes experienced by the users
and the nature of their jobs, and find that the scene sequence predicted by our
model tend to possess similar relationship. The initial promising results
reveal numerous research directions for acoustic scene classification via
wearable devices as well as egocentric analysis of dynamic acoustic scenes
encountered by the users.
