在真实的人机交互场景中自动检测呼吸障碍
Eduardo Alvarado1, Nicolás Grágeda1, Alejandro Luzanto1
1Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
这项研究适应了呼吸应急估计技术的人机交互 (HRI) 场景. 结果显示,即使有噪音数据,也具有很高的准确性,在静态和动态HRI环境中显示出强度.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 信号处理 信号处理
- 人与机器人的交互
背景情况:
- 开发了一种用于电话的先前呼吸应急估计技术.
- 现实世界的人机交互 (HRI) 场景为音频信号处理带来了独特的挑战,原因是噪音和反响.
研究的目的:
- 在静态和动态HRI设置中调整和评估呼吸应急估计技术.
- 评估环境噪音和反响对技术性能的影响.
- 为了比较不同梁成型方法和特征组合的有效性.
主要方法:
- 使用定制机器人平台重新记录了现有的呼吸应急估计系统.
- 通过电话训练数据增加了模拟的环境噪音和使用室内冲动响应 (RIR) 的反响.
- 使用精度和曲线下的面积 (AUC) 度量来评估性能.
- 应用了延迟和总和和MVDR光束成形技术.
- 分析和结合了时间依赖和时间独立的特征.
主要成果:
- 调整后的系统实现了准确性,AUC得分仅比匹配的模拟数据条件低0.4%.
- 静态和动态HRI条件之间的性能差异是最小的.
- 波束成形方法 (延迟和总和,MVDR) 平均提高了精度8%和AUC2%.
- 结合时间依赖和时间独立的特征,获得了最佳的联合精度和AUC.
结论:
- 呼吸应急估计技术强大,适应现实世界静态和动态HRI环境.
- 环境建模和光束成形技术显著提高了性能.
- 多种特征的组合提供了最有效的方法,用于HRI中准确估计呼吸困难.
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