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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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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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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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Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
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相关实验视频

Updated: Jun 16, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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开发一种智能系统,使用机器学习对失序声音进行二进制分类.

Yat Chun Au1, Manwa L Ng1

  • 1Speech Science Laboratory, Faculty of Education, 729 Meng Wah Complex, University of Hong Kong, Hong Kong, China.

American journal of otolaryngology
|May 17, 2025
PubMed
概括
此摘要是机器生成的。

机器学习模型,特别是随机森林 (RF),使用声学特征准确地分类语音障碍. 这种自动化分析提供了一种可靠的非侵入性方法,用于早期检测和改善语音护理患者的结果.

关键词:
声学分析 声学分析机器学习 机器学习语音分类 声音分类声音障碍 声音障碍语音病理学 语音病理学

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相关实验视频

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科学领域:

  • 语音和听力科学 语言和听力科学
  • 计算语言学 计算语言学
  • 生物医学工程 生物医学工程

背景情况:

  • 语音障碍源于发音过程中语音振动受到干扰,影响语音质量.
  • 准确的诊断对于有效的管理和改善患者结果至关重要.
  • 传统的诊断方法可能是主观的,耗时的.

研究的目的:

  • 探索机器学习 (随机森林和决策树模型) 的应用,用于分类规范声和失序声音.
  • 使用声学特征来比较RF和DT分类器的诊断实用性.
  • 在多语言数据库中评估个别声学参数,重点关注广东语语音样本.

主要方法:

  • 利用了1986年来自三个数据库的持续元音/a/录音,包括一个当地的广东语临床存储库.
  • 使用Parselmouth (Python接口与Praat) 提取了29个声学特征.
  • 经过训练和验证的RF和DT模型,通过精度,灵敏度,特异性和F1得分来比较性能;评估特征重要性并执行ROC分析.

主要成果:

  • 随机森林 (RF) 模型实现了89%的准确性,超过了决策树 (DT) 模型 (78%的准确性).
  • 分类的关键声学特征包括年龄,CSID,闪和动.
  • 对于男性来说,CSID和stdevF0Hz是可靠的歧视者,而对于女性来说,CSID,locallabsoluteJitter,apq11Shimmer和localdbShimmer是有效的,尽管需要对特定人口进行校准.

结论:

  • 机器学习,特别是射频算法,通过自动声学特征分析显著提高了语音障碍诊断的准确性.
  • 整合ML模型为早期检测和管理提供了可靠的,非侵入性的方法,有可能改善患者的治疗结果.
  • 进一步的研究应该集中在数据集的多样性和验证上,以提高概括性和临床适用性.