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相关概念视频

Echo01:06

Echo

1.1K
The human ear cannot distinguish between two sources of sound if they happen to reach within a specific time interval, typically 0.1 seconds apart. More than this, and they are perceived as separate sources.
Imagine the sound is reflected back to the ears. Assuming that the source is very close to the human, the difference between hearing the two sounds—the emitted sound and the reflected sound—may be more than the minimum time for perceiving distinct sounds. If this is the case,...
1.1K
Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

1.2K
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...
1.2K
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

409
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
409
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

8.7K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
8.7K
Doppler Effect - II01:05

Doppler Effect - II

5.0K
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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相关实验视频

Updated: Mar 4, 2026

Sound Source Localization Testing in Single-sided Deafness Following Bone Conduction Intervention
04:32

Sound Source Localization Testing in Single-sided Deafness Following Bone Conduction Intervention

Published on: December 20, 2024

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一个基于条件扩散的模型,用于高分辨率的声源映射.

Haobo Jia1,2, Feiran Yang3, Jianfei Tong1

  • 1Laboratory of Noise and Audio Research, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China.

The Journal of the Acoustical Society of America
|March 3, 2026
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种基于扩散的新型声源映射框架,提高了解卷精度. 生成模型有效地捕捉了源结构,在概括任务中表现优于现有方法.

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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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A Method to Study Adaptation to Left-Right Reversed Audition
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Last Updated: Mar 4, 2026

Sound Source Localization Testing in Single-sided Deafness Following Bone Conduction Intervention
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Sound Source Localization Testing in Single-sided Deafness Following Bone Conduction Intervention

Published on: December 20, 2024

948
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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A Method to Study Adaptation to Left-Right Reversed Audition
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科学领域:

  • 声学 声学 在声学方面
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 反向成像问题,特别是声源映射,由于源分布稀疏和峰形而具有挑战性.
  • 传统的监督回归方法难以准确地建模这些复杂的源结构,往往导致模糊的文物.
  • 扩散模型提供了强大的生成能力,适用于反向问题.

研究的目的:

  • 引入第一个基于扩散的声源映射框架,直接解决解卷反向问题.
  • 开发一个生成模型,明确学习源地图的结构前置,避免模糊的文物.
  • 为了提高声源映射精度和概括能力.

主要方法:

  • 一个基于扩散的生成框架,条件是延迟和总和束形状图和自动编码器提取的多尺度点扩散函数特征.
  • 使用光滑的目标图来指导模型捕获结构先验.
  • 实施时间加权损失函数,以改善训练期间的条件利用.
  • 使用自动编码器从点传播函数中提取频率感知特征.

主要成果:

  • 拟议的扩散模型成功地生成了高分辨率的声源分布图,在推断过程中只需要20个采样步骤.
  • 实验结果表明,与传统和监督回归式深度学习方法相比,其性能优越.
  • 该框架显示了在未见的频率,不同数量的源和现实世界的传输函数中强烈的概括.

结论:

  • 基于扩散的框架代表了声源映射的重大进步,克服了以前方法的局限性.
  • 该模型能够学习结构先验并避免模糊的文物导致更准确和详细的源地图.
  • 这种方法为复杂场景中的声源映射提供了强大的和可通用的解决方案.