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相关概念视频

Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by identifying...

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相关实验视频

Updated: Jun 12, 2026

Wideband Optical Detector of Ultrasound for Medical Imaging Applications
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一个高度灵敏的全向声学传感器,用于增强人机交互.

Wenyan Qiao1,2, Linglin Zhou1,2, Jiayue Zhang3

  • 1Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, P. R. China.

Advanced materials (Deerfield Beach, Fla.)
|October 16, 2024
PubMed
概括

本研究介绍了一种自动供电的 triboelectric 立体声学传感器 (SAS),用于先进的人机交互. 传感器在杂的环境中准确识别声源,改善机器人通信.

关键词:
人机交互的人机交互智能机器人 智能机器人 智能机器人一个全方位的声学传感器.声源识别和跟踪 声源识别和跟踪电流电流电流电流电流电流.

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Universal Hand-held Three-dimensional Optoacoustic Imaging Probe for Deep Tissue Human Angiography and Functional Preclinical Studies in Real Time
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科学领域:

  • 机器人技术和人机交互的人机交互
  • 材料科学和传感器技术材料科学和传感器技术
  • 声学和信号处理

背景情况:

  • 基于声学传感器的人机交互 (HMI) 对智能机器人至关重要,但在识别和跟踪全方位声音源方面面临挑战,特别是在杂的环境中.
  • 当前的系统在处理复杂的声学场景时,难以获得准确性和稳定性.

研究的目的:

  • 开发一种自动供电的三电式立体声学传感器 (SAS),具有增强的全向声音识别和跟踪能力.
  • 解决当前声学HMI系统在杂和复杂环境中的局限性.

主要方法:

  • 一个3D结构的 triboelectric立体声学传感器 (SAS) 被设计和制造.
  • 传感器使用具有特定材料特性 (高电子亲和度,低扬模量) 的多孔振动膜,以获得高灵敏度和广泛的频率响应.
  • 深度学习算法用于音频信号识别和跟踪.

主要成果:

  • 开发的SAS表现出高灵敏度 (3172.9mVpp Pa-1) 和广泛的频率响应范围 (100-20 000 Hz).
  • 在精确识别所需的音频信号方面,即使在杂的环境中,也实现了平均98%的深度学习准确度.
  • 在会议系统和自动驾驶汽车等复杂场景中,成功展示了对多个个体和驾驶命令的同时识别.

结论:

  • 自动供电的 triboelectric SAS 为基于语音的 HMI 系统提供了显著的进步.
  • 传感器的全方位识别和抗噪声能力为更自然,更有效的人机通信铺平了道路.
  • 这项技术对在具有挑战性的声学条件下需要强大的基于音频的交互的应用具有前景.