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Auditory Pathway01:15

Auditory Pathway

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Auditory pathways constitute the complex neural circuits responsible for transmitting and interpreting auditory information from the peripheral auditory system to the brain. Sound waves are initially captured by the outer ear, funneled through the ear canal, and reach the tympanic membrane (eardrum). These vibrations are transmitted via the middle ear's ossicles to the inner ear's cochlea.
When viewed cross-sectionally, the cochlea reveals the scala vestibuli and scala tympani flanking...
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Respiratory System Abnormal Finding II: Palpation and Auscultation01:31

Respiratory System Abnormal Finding II: Palpation and Auscultation

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In assessing respiratory abnormalities, palpation and auscultation are critical tools for detecting and interpreting various pathophysiological changes. These techniques provide insight into underlying disorders by evaluating tactile sensations and sounds produced by the respiratory system.
Palpation Findings
During a respiratory assessment, palpation can reveal several vital abnormalities:
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相关实验视频

Updated: May 29, 2025

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

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多维特征提取用于基于深度学习方法的语音病理检测.

Sozan Abdullah Mahmood1

  • 1Computer Department, College of Science, University of Sulaimani, Sulaimaniyah 46001, Kurdistan, Iraq.

Journal of voice : official journal of the Voice Foundation
|February 2, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的深度学习模型来检测语音病理,在识别语音障碍方面实现了高精度. 拟议的方法提高了患者的早期诊断和治疗效率.

关键词:
语音病理检测 声谱 青少年 凯泽 能量操作员 深度学习

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Last Updated: May 29, 2025

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科学领域:

  • 医学研究 医学研究
  • 信号处理 信号处理
  • 人工智能的人工智能是人工智能.

背景情况:

  • 语音病理学检测对于及时治疗语音障碍至关重要.
  • 早期诊断可以改善治疗结果,减少医疗负担.

研究的目的:

  • 开发一个先进的深度学习模型,以改进语音病理学检测.
  • 为了提高识别健康与病态声音的准确性.

主要方法:

  • 利用深度学习,特别是ResNet,用于语音病理学分类.
  • 提出了一个新的特征提取方案:将Gammatonegram与 (TKEO) Scalogram (CGT Scalogram) 结合起来.
  • 分析时间频率特征,从语音信号中提取敏感特征.

主要成果:

  • 在二进制分类中实现了96%的准确性,96.3%的精度和96.1%的回忆.
  • 在多类分类中达到94.4%的准确性,94.5%的精度和94%的回忆率.
  • 证明了CGT Scalogram特征选择技术的有效性.

结论:

  • 开发的模型显著提高了语音病理学检测准确度.
  • 拟议的特征提取方法对二进制和多类分类都有效.
  • 这种方法有望促进声音障碍的早期诊断.