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相关概念视频

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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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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Echo01:06

Echo

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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,...
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Sound Intensity Level00:53

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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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环境噪声数据集用于声音事件分类和检测.

Luca Fredianelli1, Francesco Artuso2, Geremia Pompei3

  • 1Institute for Chemical-Physical Processes of the Italian Research Council, Via Moruzzi 1, 56100, Pisa, Italy. luca.fredianelli@cnr.it.

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|October 30, 2025
PubMed
概括

本研究介绍了DataSEC和DataSED,两种用于声音事件分类和检测的开放访问数据集. 这些数据集有助于研究分析环境噪音和在现实环境中识别声音源.

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科学领域:

  • 声学和信号处理
  • 机器学习应用 机器学习应用
  • 环境科学 环境科学

背景情况:

  • 音频事件分类 (SEC) 和检测 (SED) 对于分析音频数据越来越重要.
  • 在杂的户外环境中识别声音源是一个重大挑战.
  • 现有的数据集往往在范围和真实性方面存在局限性.

研究的目的:

  • 为了引入两个新的,开放式访问数据集,DataSEC和DataSED.
  • 解决现有的声音事件数据集中发现的漏洞.
  • 支持对现实世界声音事件分类和环境噪声自动化分析的研究.

主要方法:

  • 收集了超过35个小时的真实,非合成的 .wav 音频数据.
  • 利用声级计测量和在线存储库来获取数据.
  • 结构化数据SEC具有4292个单一事件样本,跨22个类和28个子类.
  • 开发了具有712条记录和超过4000个标签的数据SED.csv格式.

主要成果:

  • 数据SEC提供分类单个声音事件.
  • 数据SED提供了具有详细事件标签的多事件记录.
  • 数据集涵盖了各种各样的城市和农村环境.
  • 这些数据集包含真实的,现实世界的音频录音.

结论:

  • 数据SEC和DataSED为SEC和SED的研究提供了宝贵的资源.
  • 这些数据集有助于开发可靠的环境声音分析算法.
  • 开放式访问的性质促进了该领域的进一步研究和开发.