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相关概念视频

Doppler Effect - I00:56

Doppler Effect - I

3.6K
The Doppler effect and Doppler shift were named after the Austrian physicist and mathematician Christian Johann Doppler in 1842, who conducted experiments with both moving sources and moving observers. Consider an observer standing on a street corner, observing an ambulance with a siren sound passing by at a constant speed. The observer experiences two characteristic changes in the sound of the siren. Initially, the sound increases in loudness as the ambulance approaches and decreases in...
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Sound Intensity00:58

Sound Intensity

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The loudness of a sound source is related to how energetically the source is vibrating, consequently making the molecules of the propagation medium vibrate. To measure the loudness of a source, the physical quantity of interest is the intensity. This is defined as the energy emitted per unit of time per unit of area perpendicular to the sound wave's propagation direction. Since the total energy is greater if the source vibrates for a longer duration and over a larger area, dividing the...
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Doppler Effect - II01:05

Doppler Effect - II

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The Doppler effect has several practical, real-world applications. For instance, meteorologists use Doppler radars to interpret weather events based on the Doppler effect. Typically, a transmitter emits radio waves at a specific frequency toward the sky from a weather station. The radio waves bounce off the clouds and precipitation and travel back to the weather station. The radio frequency of the waves reflected back to the station appears to decrease if the clouds or precipitation are moving...
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¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

1.0K
Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
1.0K
Interference: Path Lengths01:10

Interference: Path Lengths

1.3K
Consider two sources of sound, that may or may not be in phase, emitting waves at a single frequency, and consider the frequencies to be the same.
Two special sources may be considered when they are in phase. This can be easily achieved by feeding the two sources from the same source. An example would be synchronizing the two speakers by feeding them with the same source, such as the sound waves produced by a tuning fork. This setup ensures that the two sources have the same frequency and are...
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相关实验视频

Updated: Jul 11, 2025

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

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基于数据驱动的源定位的视角依赖的语音动态.

Weidi Wang1, Amir Ashkan Mokhtari2, Ankit Srivastava2

  • 1Department of Mechanical Engineering, University of Massachusetts, Lowell, Lowell, Massachusetts 01854, USA.

The Journal of the Acoustical Society of America
|November 8, 2023
PubMed
概括
此摘要是机器生成的。

这项研究使用语音阵列和特殊点来准确定位声音源. 人工神经网络准确地识别事件角度,优于传统方法.

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Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

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Infant Auditory Processing and Event-related Brain Oscillations
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科学领域:

  • 声学 声学 在声学方面
  • 材料科学 材料科学 材料科学
  • 人工智能的人工智能

背景情况:

  • 在各种传感应用中,源角度定位问题至关重要.
  • 音声晶体中的特殊点提供了独特的模态行为,以提高灵敏度.
  • 现有的本地化方法可以通过先进的信号处理技术来改进.

研究的目的:

  • 调查使用声波阵列和异常点进行二维弹性波散射分析.
  • 为准确的源角度定位开发数据驱动的方法.
  • 为了提高现有的本地化技术的性能.

主要方法:

  • 在音频阵列中分析等频带结构和角度依赖的模态行为.
  • 利用特殊的点和关键角度来提高灵敏度.
  • 用自身模式训练一个人工神经网络,用于深度学习散射动态.

主要成果:

  • 训练有素的神经网络准确地分类散射信号的事件角度.
  • 与标准的延迟和总和技术相比,拟议的方法表明了优越的本地化性能.
  • 实现了最小的侧叶水平和抑制的主叶宽度.

结论:

  • 声波阵列和特殊点为先进的传感提供了一个物理相关的平台.
  • 使用人工神经网络的数据驱动方法显著改善了源角度定位.
  • 这种方法为现有的本地化技术提供了强大的增强,利用带拓和自身模式.