超越语言的情绪：基于边缘计算的非语音音频情绪识别
Emotions Beyond Words: Non-Speech Audio Emotion Recognition With Edge Computing
AI总结：
针对现有研究多聚焦复杂架构语音情绪识别的现状，提出基于知识蒸馏的边缘计算非语音音频情绪识别框架，经公开数据集验证性能优异，为相关领域提供了新方向。
摘要：
非语音情绪识别在医疗、犯罪防控与救援、娱乐等诸多领域有着广泛应用。利用边缘计算为这些应用提供支撑具备巨大潜力，但现有研究多聚焦于采用复杂架构的语音情绪识别。本文提出了一种基于非语音的情绪识别系统，该系统可依托边缘计算，分析尖叫、哭泣等非语音表达所传递的情绪。具体而言，我们探索利用知识蒸馏（knowledge distillation）设计计算高效的系统，使其可部署在资源有限的边缘设备上，且不会显著降低性能。我们使用两个公开数据集对所提框架进行了全面评估，并将结果与知名的MobileNet模型对比以凸显其有效性。结果证明了将边缘计算用于非语音情绪检测的可行性与有效性，有望改进通信网络中依赖情绪检测的应用。据我们所知，这是首个针对非语音音频情绪检测的基于边缘计算的框架研究，为未来研究提供了有前景的方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
