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强大的声学定向传感是基于共振器的传感器和深度学习之间的协同作用所实现的
Ziqi Yu1, Xiaopeng Li2, Hojung Jung3
1Toyota Research Institute of North America, Toyota Motor North America, Ann Arbor, MI, 48105, USA. ziqi.yu@toyota.com.
Scientific reports
|May 2, 2024
概括
我们将共振器声学传感器与深度学习相结合,以增强信号检测. 这种协同作用提高了各种声音信号的定向精度,有助于自动驾驶等应用.
科学领域:
- 声学感应 声学感应 声学感应
- 深度学习是一种深度学习.
- 信号处理 信号处理
背景情况:
- 基于共振器的声学传感器为紧和敏感的检测提供了潜力.
- 深度学习模型,特别是卷积神经网络 (CNN),在噪音数据中的复杂模式识别方面表现出色.
研究的目的:
- 研究基于共振器的声学传感器和深度学习的协同效应,以增强声学传感.
- 评估组合系统在准确预测声信号的入射方向方面的性能.
主要方法:
- 在三腔共振器中对振动振幅和相增强的数值验证.
- 对单频和警报信号的传感器响应的实验测量.
- 训练卷积神经网络 (CNN),使用来自共振器传感器数据的振幅和相位特征.
主要成果:
- 振动振幅和相位都得到了增强,并在和关闭共振时被保留.
- 用振幅和相位特征训练的CNN在预测信号方向方面取得了卓越的准确性.
- 协同方法显著优于没有共振器的参考传感器,特别是在宽带和噪音信号方面.
结论:
- 响应效应和深度学习的结合为声学传感提供了一个互补的优势.
- 这种方法提高了紧的声学传感器对狭窄和宽带信号的性能.
- 开发的技术显示了先进的传感应用的前景,包括用于紧急车辆检测的自动驾驶汽车系统.
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