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强大的自动列车路过检测,结合深度学习和声音水平分析.

Erwann Betton-Ployon1,2, Abbes Kacem1, Jérôme Mars2

  • 1ACOUSTB, Saint-Martin-d'Héres, 38400, France.

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概括

这项研究引入了一种自动检测火车路过的方法,这对于控制噪音污染至关重要. 该方法实现了高精度,使得在各种环境中更好地评估铁路噪声.

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科学领域:

  • 声学和信号处理
  • 环境噪音监测 环境噪音监测

背景情况:

  • 对于控制高噪音水平的日益增长的需求,需要先进的自动声音事件检测.
  • 关于自动检测火车路过情况的研究有限,尽管它具有显著的烦因素.

研究的目的:

  • 开发一种创新和准确的方法,用于自动检测列车通过.
  • 改进在各种音景中对铁路噪声贡献的估计.

主要方法:

  • 一个通用分类器识别来自原始音频信号的车辆噪声.
  • 梅尔频谱分析和声音水平指标完善检测,以隔离列车通过.
  • 该方法在各种长期音频信号上进行了测试.

主要成果:

  • 实现了90%的时间重叠与火车路过事件的参考划界.
  • 在多种多样的长期音频录音中,已经证明了很高的检测率.
  • 开发的技术有效地区分火车噪音和其他车辆声音.

结论:

  • 拟议的方法提供了一个可靠的解决方案,用于自动检测列车通过.
  • 高度的检测准确度有助于精确地评估铁路噪声贡献.
  • 这一进步有助于更好地管理城市和农村声音环境中的噪音污染.