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相关概念视频

Perception of Sound Waves01:01

Perception of Sound Waves

4.4K
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...
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Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

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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...
205

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A Method to Study Adaptation to Left-Right Reversed Audition
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通过深度学习恢复空间声学特性.

Ruixian Liu1, Peter Gerstoft1

  • 1Department of Electrical and Computer Engineering, University of California, San Diego, California 92161, USA.

The Journal of the Acoustical Society of America
|June 7, 2024
PubMed
概括

空间依赖物理信息的神经网络 (SD-PINNs) 可以从测量中恢复空间变化的部分微分方程 (PDE) 系数. 这种方法消除了对领域专业知识的需求,并揭示了不均介质中的声学特性.

科学领域:

  • 计算物理学的计算物理.
  • 机器学习应用程序 机器学习应用程序
  • 物理学中的反向问题.

背景情况:

  • 传统的基于物理学的神经网络 (PINNs) 擅长恢复常数偏微分方程 (PDE) 系数.
  • 恢复空间变化系数通常需要特定领域的专业知识或多个模型.
  • 在有效地从有限的数据中识别空间异质材料特性方面存在差距.

研究的目的:

  • 引入一个新的空间依赖物理信息神经网络 (SD-PINN),用于恢复空间变化的PDE系数.
  • 为了证明SD-PINN在没有先前的特定领域物理知识的情况下发挥作用的能力.
  • 将SD-PINN应用于使用波方程来描述不均介质中的声学特性.

主要方法:

  • 开发了一个SD-PINN架构,能够推断空间域内变化的系数.
  • 通过最小化一个结合损失函数来训练网络,并结合了来自管理PDE的数据拟合和物理约束.
  • 在二维 (2D) 区域中使用低级矩阵假设PDE系数,以便在未测量的位置进行恢复.

主要成果:

  • SD-PINN成功地恢复了波方程的空间依赖系数.
  • 该方法证明了有效的系数回收,即使在稀疏或没有直接测量的地区.

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  • 在一个不均的介质中,声学特性的空间分布成功地被揭示出来.

    • 结论:

    • SD-PINNs提供了一种强大的,统一的方法来识别空间变化的PDE系数.
    • 这种方法显著减少了对逆向问题的专业物理专业知识的依赖.
    • SD-PINN框架对材料的表征和理解复杂的物理现象具有前景.