城市噪音源的智能分类使用TinyML:向智能城市的高效噪音管理
Maykol Sneyder Remolina Soto1, Brian Amaya Guzmán1, Pedro Antonio Aya-Parra2,3
1School of Science and Engineering, Universidad del Rosario, Bogotá 111711, Colombia.
Sensors (Basel, Switzerland)
|October 29, 2025
概括
这项研究表明,微型机器学习 (TinyML) 对于实时城市噪音监测是有效的. 该系统准确地识别汽车等噪音源,改善智能城市噪音管理和公共卫生政策.
科学领域:
- 环境科学 环境科学
- 声学 声学 在声学方面
- 机器学习 机器学习
背景情况:
- 城市噪音污染超过世界卫生组织 (WHO) 在波哥大限制,影响公共健康.
- 波哥大11.8%的人口暴露于高于世卫组织建议的噪音水平.
研究的目的:
- 开发和评估嵌入式智能系统,用于实时识别和分类环境噪声源.
- 评估在设备上的微型机器学习 (TinyML) 对于城市声学监控的可行性.
主要方法:
- 在八个噪音类别中收集并标记了657个音频片段.
- 在Raspberry Pi 2W上实现了一个TinyML模型,用于自主,设备上的音频处理.
- 使用 60/20/20 列车验证测试分割,确保跨子集的数据完整性.
主要成果:
- 在现实城市条件下,TinyML模型实现了高精度和回忆 (0.92-1.00).
- 重型车辆和摩托车是最常见的噪音来源.
- 飞机噪音事件虽然较少,但达到88.4dB (A),明显超过当地限值.
结论:
- 在设备上的TinyML分类是城市噪音监测的可行和高效方法.
- 当地推断将延迟,带宽和隐私问题最小化,支持可扩展的智能城市解决方案.
- 这种方法为基于证据的公共政策提供了基础,以提高城市福祉.
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