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相关概念视频

Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

204
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...
204
Classification of Signals01:30

Classification of Signals

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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Sound Intensity Level00:53

Sound Intensity Level

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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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Heart Sounds01:15

Heart Sounds

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Heart sounds are generated by the turbulence in blood flow due to the closing of heart valves. These sounds are best perceived slightly away from the valves, where the blood flow disseminates the sound.
Auscultation is the process of listening to these internal body sounds using a stethoscope. The heart produces four types of sounds, but only two—S1 and S2—can usually be heard with a stethoscope.
S1, also known as the "lub" sound, is caused by the closure of atrioventricular (A-V)...
1.9K
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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Hearing01:31

Hearing

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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.
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相关实验视频

Updated: Jun 21, 2025

Flying Insect Detection and Classification with Inexpensive Sensors
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声源分类用于声音景观分析,使用来自城市声学传感器网络的快速第三八度频段数据)

Modan Tailleur1, Pierre Aumond2, Mathieu Lagrange1

  • 1Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, Nantes, F-44000, France.

The Journal of the Acoustical Society of America
|July 16, 2024
PubMed
概括

这项研究表明,预训练的音频神经网络 (PANN) 可以有效地识别使用较低分辨率的城市噪声数据的声音源. 一个开发的转码器使PANN能够处理快速的第三八度,保持用于音景监控的分类器性能.

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相关实验视频

Last Updated: Jun 21, 2025

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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:

  • 声景探索需要准确的声音源表征.
  • 预训练有素的音频神经网络 (PANN) 擅长识别500多个声音源.
  • 城市噪声传感器通常提供低分辨率的快速三八度数据,与PANN的高分辨率Mel频谱-时间输入要求不兼容.

研究的目的:

  • 在转换后使用快速第三八度数据评估PANN的性能.
  • 评估先前开发的转码器在适应低分辨率音频数据的有效性.
  • 探索这种方法在城市音景监测中的潜力.

主要方法:

  • 利用先前开发的转码器将快速的第三八度数据转换为Mel光谱-时间表示.
  • 使用预训练有素的音频神经网络 (PANN) 与转码的数据进行声音源预测.
  • 对快速第三八度录音的大型数据集进行了定性分析.

主要成果:

  • 在使用转码快三八度数据时,PANNs在预测声音源存在的感知时间方面的表现并没有显著下降.
  • 转码器有效地适应了低分辨率的音频数据,以便使用PANN.
  • 该研究证实了使用随时可用的城市噪声数据的PANN的可行性.

结论:

  • 经过预训练的音频神经网络可以通过数据转换方法有效地利用低分辨率的城市噪声数据来监测声音景观.
  • 这种方法保持了声音源分类的完整性,为城市声环境分析提供了新的可能性.
  • 这些发现支持使用现有传感器网络开发先进的城市音景监测系统.