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Related Concept Videos

The Cochlea01:13

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The cochlea is a coiled structure in the inner ear that contains hair cells—the sensory receptors of the auditory system. Sound waves are transmitted to the cochlea by small bones attached to the eardrum called the ossicles, which vibrate the oval window that leads to the inner ear. This causes fluid in the chambers of the cochlea to move, vibrating the basilar membrane.
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Related Experiment Video

Updated: Jul 4, 2025

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages
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Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages

Published on: March 24, 2023

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Classification of Adventitious Sounds Combining Cochleogram and Vision Transformers.

Loredana Daria Mang1, Francisco David González Martínez1, Damian Martinez Muñoz1

  • 1Department of Telecommunication Engineering, University of Jaen, 23700 Linares, Spain.

Sensors (Basel, Switzerland)
|January 26, 2024
PubMed
Summary

This study shows that combining cochleograms with Vision Transformers (ViT) significantly improves respiratory sound classification for early disease detection. This novel approach enhances the accuracy of identifying lung abnormalities from breathing sounds.

Keywords:
accuracyadventitious soundsclassificationcochleogramdeep learningvision transformers

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Area of Science:

  • Medical Informatics
  • Signal Processing
  • Artificial Intelligence

Background:

  • Early detection of respiratory irregularities is crucial for improving lung health and reducing mortality.
  • Respiratory sound analysis is a key method for assessing lung condition and identifying abnormalities.

Purpose of the Study:

  • To investigate the performance of the Vision Transformer (ViT) architecture using cochleograms as input for adventitious respiratory sound classification.
  • To evaluate the novel combination of cochleogram input with ViT for respiratory sound analysis.

Main Methods:

  • The study utilized the ICBHI dataset for evaluating the proposed methodology.
  • Respiratory sounds were represented as cochleograms and fed into the ViT architecture.
  • Classification performance was compared against state-of-the-art Convolutional Neural Network (CNN) approaches using various input features (spectrogram, MFCC, CQT, cochleogram).

Main Results:

  • The combination of cochleogram input with the ViT architecture demonstrated superior classification performance compared to other methods.
  • ViT, when applied to cochleograms, showed significant potential for reliable respiratory sound classification.
  • The results confirm the effectiveness of this novel input-classifier pairing for adventitious sound classification.

Conclusions:

  • The findings highlight the potential of ViT with cochleogram input for accurate and efficient respiratory sound classification.
  • This research contributes to the development of intelligent automated techniques for augmenting the speed and effectiveness of respiratory disease detection.
  • The study addresses a critical need for improved diagnostic tools in respiratory medicine.