Real-Time Sound Event Localization and Detection: Deployment Challenges on Edge Devices

Jun Wei Yeow, Ee-Leng Tan, Jisheng Bai, Santi Peksi, Woon-Seng Gan
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Abstract

Sound event localization and detection (SELD) is critical for various real-world applications, including smart monitoring and Internet of Things (IoT) systems. Although deep neural networks (DNNs) represent the state-of-the-art approach for SELD, their significant computational complexity and model sizes present challenges for deployment on resource-constrained edge devices, especially under real-time conditions. Despite the growing need for real-time SELD, research in this area remains limited. In this paper, we investigate the unique challenges of deploying SELD systems for real-world, real-time applications by performing extensive experiments on a commercially available Raspberry Pi 3 edge device. Our findings reveal two critical, often overlooked considerations: the high computational cost of feature extraction and the performance degradation associated with low-latency, real-time inference. This paper provides valuable insights and considerations for future work toward developing more efficient and robust real-time SELD systems
实时声音事件定位和检测:边缘设备的部署挑战
声音事件定位和检测(SELD)对于智能监控和物联网(IoT)系统等各种真实世界应用至关重要。虽然深度神经网络(DNN)是最先进的声音事件定位和检测方法,但其显著的计算复杂性和模型大小给在资源有限的边缘设备上部署带来了挑战,尤其是在实时条件下。尽管对实时 SELD 的需求日益增长,但这一领域的研究仍然有限。在本文中,我们通过在商用 Raspberry Pi 3 边缘设备上进行大量实验,研究了在真实世界实时应用中部署 SELD 系统所面临的独特挑战。我们的研究结果揭示了两个经常被忽视的关键因素:特征提取的高计算成本和与低延迟、实时推理相关的性能下降。本文为今后开发更高效、更稳健的实时 SELD 系统提供了宝贵的见解和考虑因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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