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BSN-ESC:用于AIoT应用的大小网络环境声音分类方法
Lujie Peng1, Junyu Yang1, Longke Yan1
1Department of Internet of Things Engineering, School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
一个新的BSN-ESC模型显著降低了环境声音分类 (ESC) 的计算复杂性. 这使得AIoT设备的效率更高,同时保持高精度,使声音分析的应用更广泛.
科学领域:
- 人工智能的人工智能
- 物联网 (AIoT) 的互联网
- 信号处理 信号处理
背景情况:
- 环境声音分类 (ESC) 对AIoT应用至关重要.
- 现有的ESC方法是计算密集型的,限制了AIoT的部署.
- 对于资源有限的设备,需要高效的ESC模型.
研究的目的:
- 开发一种具有高精度和低计算复杂性的新型ESC方法 (BSN-ESC).
- 为了使ESC能够在资源有限的AIoT设备上部署.
主要方法:
- 提出了一个基于大小网络 (BSN) 的ESC模型,该模型根据分类难度自适应地选择网络大小.
- 引入了一种预分类技术,使用日志-梅尔谱图精炼来防止频率扭曲.
- 在CPU和FPGA上使用ESC-50数据集实施和评估BSN-ESC模型.
主要成果:
- 实现了显著降低的计算复杂性 (0.123G FLOPs),比最先进的技术提高了2309倍.
- 在ESC-50数据集上保持了89.25%的高分类准确度.
- 在PC和嵌入式系统上都表现出有效的性能.
结论:
- 在资源有限的AIoT设备上,BSN-ESC模型为高效的ESC提供了可行的解决方案.
- 提出的方法成功地平衡了计算效率和分类性能.
- 这项工作有助于将先进的声音分析集成到边缘计算应用中.
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