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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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Force Classification01:22

Force Classification

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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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Perception of Sound Waves01:01

Perception of Sound Waves

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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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Wave Parameters01:10

Wave Parameters

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The simplest mechanical waves are associated with simple harmonic motion and repeat themselves for several cycles. These simple harmonic waves can be modeled using a combination of sine and cosine functions. Consider a simplified surface water wave that moves across the water's surface. Unlike complex ocean waves, in surface water waves, water moves vertically, oscillating up and down, whereas the disturbance of the wave moves horizontally through the medium. If a seagull is floating on the...
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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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Classification of Systems-II01:31

Classification of Systems-II

137
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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相关实验视频

Updated: Jun 12, 2025

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

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使用Wav2vec 2.0特征提取的语音障碍分类

Jie Cai1, Yuliang Song2, Jianghao Wu1

  • 1Department of Otorhinolaryngology, Head and Neck Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China.

Journal of voice : official journal of the Voice Foundation
|September 26, 2024
PubMed
概括
此摘要是机器生成的。

这项研究有效地使用 wav2vec 2.0 对特征提取和机器学习模型进行正常和病态声音的分类. 随机森林模型在语音分析中表现出卓越的准确性和稳定性.

关键词:
机器学习 机器学习语音障碍是一种声音障碍.波2vec 2.0 波2vec 2.0 是一个

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相关实验视频

Last Updated: Jun 12, 2025

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

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Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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科学领域:

  • 语音处理和生物医学信号分析.
  • 深度学习和机器学习在医疗保健中的应用.

背景情况:

  • 准确的语音分类对于诊断各种病理状况至关重要.
  • 传统方法可能缺乏复杂语音模式所需的特征提取能力.

研究的目的:

  • 用 wav2vec 2.0 来进行特征提取来分类正常与病态声音.
  • 评估不同机器学习分类器在此任务中的表现.

主要方法:

  • 利用 wav2vec 2.0 模型从语音录音中提取特征.
  • 训练并评估了支持向量机 (SVM),K-最近邻居,决策树 (DT) 和随机森林 (RF) 模型.
  • 采用分层K折交叉验证和各种性能指标,包括准确性,精度,回忆,F1得分,ROC曲线和混矩阵.

主要成果:

  • 随机森林 (RF) 模型实现了最高的精度 (0.98 ± 0.02) 和其他指标的强性能.
  • 决策树 (DT) 模型显示出极好的精度和平衡的性能.
  • 数据增强显著提高了所有模型的性能,特别是RF和DT.

结论:

  • wav2vec 2.0和机器学习模型的结合对语音分类非常有效.
  • 在区分正常声音和病态声音方面取得了卓越的准确性和稳定性.
  • 射频和DT模型特别适合于这种语音分类任务.