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arXiv · 2409.11700

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

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Jun Wei Yeow, Ee-Leng Tan, Jisheng Bai, Santi Peksi, Woon-Seng Gan. 2024-09-18. Real-Time Sound Event Localization and Detection: Deployment Challenges on Edge Devices. https://arxiv.org/abs/2409.11700

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