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.

翻译：非语音情感识别在医疗保健、犯罪控制与救援以及娱乐等领域具有广泛应用。利用边缘计算实现这些应用具有巨大潜力，然而近年研究主要集中于采用复杂架构的语音情感识别。本文提出一种基于非语音的情感识别系统，该系统可依靠边缘计算分析通过尖叫、哭泣等非语言表达传递的情感。具体而言，我们探索知识蒸馏技术，设计出计算高效的神经网络架构，使其能在资源受限的边缘设备上部署且性能无明显下降。我们使用两个公开数据集对所提框架进行全面评估，并通过与知名MobileNet模型的结果对比突显其有效性。实验结果表明，采用边缘计算进行非语音情感检测具备可行性与有效性，可显著改善依赖通信网络情感检测的应用性能。据我们所知，这是首个基于边缘计算框架实现非语音音频情感检测的研究工作，为未来研究提供了有前景的发展方向。