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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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Classification of Systems-I01:26

Classification of Systems-I

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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:
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Classification of Systems-II01:31

Classification of Systems-II

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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,
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Force Classification01:22

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Perceiving Loudness, Pitch, and Location01:21

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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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Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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相关实验视频

Updated: Jul 23, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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评估预训练的卷积神经网络在嵌入式系统上进行音频分类的性能,用于智能城市的异常检测.

Mimoun Lamrini1,2, Mohamed Yassin Chkouri2, Abdellah Touhafi1,3

  • 1Department of Engineering Sciences and Technology (INDI), Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium.

Sensors (Basel, Switzerland)
|July 14, 2023
PubMed
概括

这项研究评估了预先训练的模型,用于嵌入式设备 (如Raspberry Pi) 上的环境声音识别. 结果显示有效的转移学习,使智能城市的高效实时应用成为可能.

关键词:
深度学习是一种深度学习.嵌入式系统嵌入式系统环境声音识别环境声音识别预先训练有素的模型.

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 环境声音识别 (ESR) 对智能城市至关重要,它利用机器学习 (ML) 分类器进行音频分类.
  • 在资源有限的嵌入式设备上部署深度学习 (DL) 模型带来了重大挑战.

研究的目的:

  • 评估现有的预训练模型,用于在Raspberry Pi (RPi) 和Tensor处理单元 (TPU) 上部署环境声音识别 (ESR).
  • 探索重新培训参数对不同数据集的声音分类性能的影响.

主要方法:

  • 一个现有的预训练DL模型被评估在RPI和TPU平台上部署.
  • 在笔记本电脑上对三个数据集 (ESC-10,BDLib,城市声音) 的声音分类性能进行了比较,RPi和RPi与Coral TPU.
  • 研究了再培训参数的影响.

主要成果:

  • 笔记本电脑的准确率高达99% (ESC-10: 96.6%,BDLib: 100%,城市声音: 99%).
  • 在RPi上,准确率为96.4% (ESC-10),100% (BDLib) 和95.3% (城市声音).
  • 在使用珊瑚TPU的RPI上,准确率为95.7% (ESC-10),100% (BDLib) 和95.4% (城市声音).

结论:

  • 预先训练的模型是有效的转移学习在ESR的嵌入式系统.
  • 使用预训练模型减少了计算需求,促进了更快的推断和实时应用.
  • 这种方法加速了嵌入式AI解决方案的开发,部署和性能.