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相关概念视频

Hearing01:31

Hearing

When we hear a sound, our nervous system is detecting sound waves—pressure waves of mechanical energy traveling through a medium. The frequency of the wave is perceived as pitch, while the amplitude is perceived as loudness.
Perception of Sound Waves01:01

Perception of Sound Waves

The human ear is not equally sensitive to all frequencies in the audible range. It may perceive sound waves with the same pressure but different frequencies as having different loudness. Moreover, the perception of sound waves depends on the health of an individual's ears, which decays with age. The health of one's ears may also be affected by regular exposure to loud noises.
The pitch of a sound depends on the frequency and the pressure amplitude of the source. Two sounds of the same frequency...

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相关实验视频

Updated: Jun 5, 2026

Data Acquisition and Analysis In Brainstem Evoked Response Audiometry In Mice
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Data Acquisition and Analysis In Brainstem Evoked Response Audiometry In Mice

Published on: May 10, 2019

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使用基于深度学习的框架,自动识别听觉脑干响应波形.

Sichao Liang1, Jia Xu1, Haixu Liu2

  • 1Department of Otolaryngology, Head and Neck Surgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
|June 1, 2024
PubMed
概括

包括Light-MLP和Wide&Deep在内的深度学习模型在识别听觉脑干响应 (ABR) 波形方面表现出高准确性. 这些先进的框架改善了ABR分析,适用于所有年龄和听力能力不同的人.

关键词:
宽&深模型模型听觉脑干反应的响应深度学习是一种深度学习.波形自动识别 波形自动识别

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Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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相关实验视频

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科学领域:

  • 听力学 听力学是指听力学.
  • 人工智能的人工智能
  • 生物医学工程 生物医学工程

背景情况:

  • 听觉脑干反应 (ABR) 波形识别对于听觉评估至关重要,但对某些人群来说可能具有挑战性.
  • 深度学习为提高ABR分析的准确性和效率提供了潜在的解决方案.

研究的目的:

  • 调查深度学习框架对自动ABR波形识别的有效性.
  • 评估不同年龄组和听力损失水平的表现.

主要方法:

  • 一项描述性研究分析了100名参与者的纯音调听力测量和ABR数据.
  • 使用时间域,频域ABR信号和人口统计数据生成特征向量.
  • 一个增强的Wide&Deep模型包含一个光多层感知子 (MLP),用于ABR波形识别.

主要成果:

  • 轻MLP模型实现了95.4%的加权平均精度.
  • 宽深模型实现了91.0%的加权平均准确度.
  • 这两种模型在不同参与者群体中都显示出高的识别准确性.

结论:

  • 深度学习模型,特别是Light-MLP和Wide&Deep,显示出对准确的ABR波形识别的重大前景.
  • 在这项研究中,轻MLP模型的表现优于宽深模型.
  • 进一步增强数据可能会提高Wide&Deep模型的性能.