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相关概念视频

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...
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Tapes are essential in surveying for accurate, durable, and short-distance measurements. Made from lightweight, nylon-coated steel, they offer flexibility and strength for rugged outdoor use. The nylon coating protects against rust and wear, extending the tape's life. Standard lengths, around 30 meters, are marked in meters and millimeters for precision.Surveyors select tapes based on site conditions and accuracy needs. Lightweight, nylon-coated tapes are commonly used for ease of handling and...
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Humans perceive sound by hearing. The human ear helps sound waves reach the brain, which then interprets the waves and creates the perception of hearing. The loudness of the environment in which a person is located determines whether they can distinguish between different sound sources.
The human ear can perceive an extensive range of sound intensity, necessitating the use of the logarithmic scale to define a physical quantity—the intensity level. It is a ratio of two intensities and...
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相关实验视频

Updated: Jul 23, 2025

fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals
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基于城市麦克风网络的先进噪声指示器映射.

Timothy Van Renterghem1, Valentin Le Bescond2, Luc Dekoninck1

  • 1WAVES Research Group, Department of Information Technology, Ghent University, Technologiepark 126, B 9052 Gent-Zwijnaarde, Belgium.

Sensors (Basel, Switzerland)
|July 14, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的方法,用于绘制全市道路交通噪声,使用街道数据和麦克风,绕过直接交通计数. 该方法准确地预测噪声水平和事件,有助于环境噪声影响评估.

关键词:
环境噪音映射环境噪音映射麦克风 麦克风 麦克风噪声指示器 噪声指示器噪音监测网络 噪音监测网络道路交通噪声 道路交通噪声

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科学领域:

  • 环境科学 环境科学
  • 声学 声学 在声学方面
  • 城市规划 城市规划

背景情况:

  • 准确的全市道路交通噪音映射对于城市规划和环境影响评估至关重要.
  • 现有的方法往往需要大量的交通数据,这些数据可能很难获得.
  • 需要有效且易于使用的噪音映射方法.

研究的目的:

  • 开发和验证全市道路交通噪声指示器映射的新方法.
  • 通过使用街道分类和麦克风网络,绕过直接访问交通数据的需求.
  • 将确定性建模与机器学习集成在一起,以提高噪音预测的准确性.

主要方法:

  • 简化动态交通模型被用作确定性噪声预测的基础.
  • 通过结合CNOSSOS和QSIDE模型的各个方面来建模声音传播.
  • 人工神经网络 (ANN) 用于对来自麦克风网络的测量数据进行确定性预测.

主要成果:

  • 该方法在巴塞罗那测量噪音水平的2-3dB内实现了预测准确性.
  • 噪音事件的数量可以预测,准确度约为30%.
  • 噪声指标可以在各种时间尺度上准确预测,包括每小时.

结论:

  • 提出的方法为全市道路交通噪音映射提供了强大且数据效率高的方法.
  • 这种技术使得在环境噪声影响评估中能够纳入各种各样的噪声指标.
  • 确定性模型和机器学习的整合对城市声学监测有很大的希望.