评估预训练的卷积神经网络在嵌入式系统上进行音频分类的性能,用于智能城市的异常检测
Mimoun Lamrini1,2, Mohamed Yassin Chkouri2, Abdellah Touhafi1,3
1Department of Engineering Sciences and Technology (INDI), Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
这项研究评估了预先训练的模型,用于嵌入式设备 (如Raspberry Pi) 上的环境声音识别. 结果显示有效的转移学习,使智能城市的高效实时应用成为可能.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 环境声音识别 (ESR) 对智能城市至关重要,它利用机器学习 (ML) 分类器进行音频分类.
- 在资源有限的嵌入式设备上部署深度学习 (DL) 模型带来了重大挑战.
研究的目的:
- 评估现有的预训练模型,用于在Raspberry Pi (RPi) 和Tensor处理单元 (TPU) 上部署环境声音识别 (ESR).
- 探索重新培训参数对不同数据集的声音分类性能的影响.
主要方法:
- 一个现有的预训练DL模型被评估在RPI和TPU平台上部署.
- 在笔记本电脑上对三个数据集 (ESC-10,BDLib,城市声音) 的声音分类性能进行了比较,RPi和RPi与Coral TPU.
- 研究了再培训参数的影响.
主要成果:
- 笔记本电脑的准确率高达99% (ESC-10: 96.6%,BDLib: 100%,城市声音: 99%).
- 在RPi上,准确率为96.4% (ESC-10),100% (BDLib) 和95.3% (城市声音).
- 在使用珊瑚TPU的RPI上,准确率为95.7% (ESC-10),100% (BDLib) 和95.4% (城市声音).
结论:
- 预先训练的模型是有效的转移学习在ESR的嵌入式系统.
- 使用预训练模型减少了计算需求,促进了更快的推断和实时应用.
- 这种方法加速了嵌入式AI解决方案的开发,部署和性能.
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