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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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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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Related Experiment Video

Updated: May 29, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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Multi-Dimensional Features Extraction for Voice Pathology Detection Based on Deep Learning Methods.

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
Summary
This summary is machine-generated.

This study introduces a novel deep learning model for voice pathology detection, achieving high accuracy in identifying voice disorders. The proposed method enhances early diagnosis and treatment effectiveness for patients.

Keywords:
Voice pathology detection—Scalogram—Teager_Kaiser_Energy Operator—Deep learning

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Area of Science:

  • Medical research
  • Signal processing
  • Artificial intelligence

Background:

  • Voice pathology detection is crucial for timely treatment of voice disorders.
  • Early diagnosis improves treatment outcomes and reduces healthcare burdens.

Purpose of the Study:

  • To develop an advanced deep learning model for improved voice pathology detection.
  • To enhance the accuracy of identifying healthy versus pathological voices.

Main Methods:

  • Utilized deep learning, specifically ResNet, for voice pathology classification.
  • Proposed a novel feature extraction scheme: Combine Gammatonegram with (TKEO) Scalogram (CGT Scalogram).
  • Analyzed time-frequency characteristics for sensitive feature extraction from voice signals.

Main Results:

  • Achieved 96% accuracy, 96.3% precision, and 96.1% recall in binary classification.
  • Attained 94.4% accuracy, 94.5% precision, and 94% recall in multi-class classification.
  • Demonstrated the effectiveness of the CGT Scalogram feature selection technique.

Conclusions:

  • The developed model significantly improves voice pathology detection accuracy.
  • The proposed feature extraction method is effective for both binary and multi-class classification.
  • This approach holds promise for advancing early diagnosis of voice disorders.