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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...
532
Classification of Systems-I01:26

Classification of Systems-I

215
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
215
Classification of Systems-II01:31

Classification of Systems-II

177
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
177
Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

239
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...
239
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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Perception of Sound Waves01:01

Perception of Sound Waves

4.5K
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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相关实验视频

Updated: Jul 19, 2025

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智能城市环境声音的自动分类系统

Dongping Zhang1, Ziyin Zhong1, Yuejian Xia1

  • 1Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, China Jiliang University, Hangzhou 310018, China.

Sensors (Basel, Switzerland)
|August 12, 2023
PubMed
概括

这项研究引入了环境声音分类 (ESC) 的双残余网络,通过融合音频特征来提高准确性. 该方法提高了智能城市应用中的城市噪声识别.

关键词:
卷积神经网络是一种卷积神经网络.处理数据的数据处理.环境声音分类环境声音分类剩余网络的剩余网络

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相关实验视频

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科学领域:

  • 计算机科学 计算机科学
  • 信号处理 信号处理
  • 人工智能的人工智能

背景情况:

  • 智能城市倡议越来越多地整合了人工智能和物联网等先进技术.
  • 环境声音分类 (ESC) 面临着由于非静止音频和城市噪声干扰的挑战.
  • 现有的深度学习方法在单个输入特征提取方面扎,以获得准确的ESC.

研究的目的:

  • 在智能城市环境中提高环境声音分类 (ESC) 的准确性.
  • 解决复杂的城市声学环境中特征提取的局限性.
  • 为强大的音频事件识别提出一个改进的深度学习模型.

主要方法:

  • 一个双剩余网络 (双重重网) 架构被开发用于功能融合.
  • 为了预处理较短的音频数据,引入了一个循环填充方法.
  • 使用时间频率数据增强来减轻过度匹配和扩展数据集.
  • 抽取和融合了Log-Mel光谱图和日志光谱图的特征.

主要成果:

  • 拟议的双重重网模型在UrbanSound8k数据集上显示了更好的分类准确性.
  • 从双输入分支的功能融合导致了更全面的音频表示.
  • 循环填充和数据增强技术增强了数据实用性和模型概括性.
  • 对比实验显示,与其他现有模型相比,性能优越.

结论:

  • 双重重网方法有效地提高了环境声音分类的准确性.
  • 功能融合和先进的预处理技术对于处理杂的城市音频至关重要.
  • 这种方法为智能城市环境中的音频分析提供了有前途的解决方案.