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Perception of Sound Waves01:01

Perception of Sound Waves

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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...
4.6K
Beats01:09

Beats

750
The study of music provides many examples of the superposition of waves and the constructive and destructive interference that occurs. Very few examples of music being performed consist of a single source playing a single frequency for an extended period of time. A single frequency of sound for an extended period might be monotonous to the point of irritation, similar to the unwanted drone of an aircraft engine or a loud fan. Music is pleasant and exciting due to mixing the changing frequencies...
750
Sound Waves: Interference00:53

Sound Waves: Interference

3.9K
Sound waves can be modeled either as longitudinal waves, wherein the molecules of the medium oscillate around an equilibrium position, or as pressure waves. When two identical waves from the same source superimpose on each other, the combination of two crests or two troughs results in amplitude reinforcement known as constructive interference. If two identical waves, that are initially in phase, become out of phase because of different path lengths, the combination of crests with troughs...
3.9K
Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

420
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...
420
Hearing01:31

Hearing

53.0K
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.
53.0K
Sound as Pressure Waves01:17

Sound as Pressure Waves

2.5K
Sound waves, which are longitudinal waves, can be modeled as the displacement amplitude varying as a function of the spatial and temporal coordinates. As a column of the medium is displaced, its successive columns are also displaced. As the successive displacements differ relatively, a pressure difference with the surrounding pressure is created. The gauge pressure varies across the medium.
The pressure fluctuation depends on the difference in displacements between the successive points in the...
2.5K

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Updated: Sep 9, 2025

Targeted Training of Ultrasonic Vocalizations in Aged and Parkinsonian Rats
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Targeted Training of Ultrasonic Vocalizations in Aged and Parkinsonian Rats

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分解和建模声学信号以识别工业声景中的机械缺陷

Christof Pichler1, Markus Neumayer1, Bernhard Schweighofer1

  • 1Christian Doppler Laboratory for Measurement Systems for Harsh Operating Conditions, Institute of Electrical Measurement and Sensor System, Graz University of Technology, Inffeldgasse 33, 8010 Graz, Austria.

Sensors (Basel, Switzerland)
|August 28, 2025
PubMed
概括

一种基于物理的基于声的状态监测 (ASCM) 方法在杂的工业环境中优于传统的音频功能. 这种强大的故障检测方法为工业监控提供了更高的可靠性和更广泛的应用.

关键词:
声学信号检测故障特性工程高噪声信号分解信号建模信号处理

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科学领域:

  • 声学工程
  • 信号处理
  • 机器学习
  • 工业状况监测

背景情况:

  • 基于声音的状态监测 (ASCM) 系统在受控环境中是有效的,但由于噪音和数据限制,在现实世界的工业环境中难以实现.
  • 现有的方法经常受到干扰声音和操作变异性造成的性能下降和高假阳性率的影响.
  • 纯粹基于数据的方法无法解释工业环境的复杂声学特征.

研究的目的:

  • 开发一种用于工业声学条件监测的新型故障检测方法,利用潜在的物理信号特征.
  • 在杂和可变的工业声音环境中克服传统数据驱动方法的局限性.
  • 提高声学状态监测系统的稳定性和可靠性.

主要方法:

  • 研究了声信号的物理组成部分,将与故障相关的声音建模为指数级衰减的振荡.
  • 开发了一个基于物理的信号模型,与纯粹数据驱动的技术不同.
  • 使用基于衍生物理模型的通用概率测试 (GLRT) 实施了强大的故障检测方法.

主要成果:

  • 基于模型的GLRT方法在高噪声条件下表现出优于标准音频功能,在合成和现实钢铁行业数据上得到验证.
  • 接收器操作特征 (ROC) 分析显示,GLRT方法显著优于音频功能,部分曲线下面面积 (pAUC) 超过最佳音频功能的两倍.
  • 模拟证实了低至-13dB的信号噪声比 (SNR),超过了以-10dB为限的基于音频特征的检测.

结论:

  • 提出的基于物理信息的模型方法为声学状况监测提供了更可靠和更强大的解决方案.
  • 与传统音频特征相比,GLRT方法实现的假阳性率明显较低,特别是在具有挑战性的工业环境中.
  • 该方法的物理性质允许将其推广到具有类似故障特征的其他工业场景,从而扩大其适用性.