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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2409.11700 |
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| _version_ | 1866914951748124672 |
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| author | Yeow, Jun Wei Tan, Ee-Leng Bai, Jisheng Peksi, Santi Gan, Woon-Seng |
| author_facet | Yeow, Jun Wei Tan, Ee-Leng Bai, Jisheng Peksi, Santi Gan, Woon-Seng |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_11700 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Real-Time Sound Event Localization and Detection: Deployment Challenges on Edge Devices Yeow, Jun Wei Tan, Ee-Leng Bai, Jisheng Peksi, Santi Gan, Woon-Seng Signal Processing 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 |
| title | Real-Time Sound Event Localization and Detection: Deployment Challenges on Edge Devices |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2409.11700 |