  Non-speech emotion recognition has a wide range of applications including
healthcare, crime control and rescue, and entertainment, to name a few.
Providing these applications using edge computing has great potential, however,
recent studies are focused on speech-emotion recognition using complex
architectures. In this paper, a non-speech-based emotion recognition system is
proposed, which can rely on edge computing to analyse emotions conveyed through
non-speech expressions like screaming and crying. In particular, we explore
knowledge distillation to design a computationally efficient system that can be
deployed on edge devices with limited resources without degrading the
performance significantly. We comprehensively evaluate our proposed framework
using two publicly available datasets and highlight its effectiveness by
comparing the results with the well-known MobileNet model. Our results
demonstrate the feasibility and effectiveness of using edge computing for
non-speech emotion detection, which can potentially improve applications that
rely on emotion detection in communication networks. To the best of our
knowledge, this is the first work on an edge-computing-based framework for
detecting emotions in non-speech audio, offering promising directions for
future research.
