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Hearing01:31

Hearing

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When we hear a sound, our nervous system is detecting sound waves—pressure waves of mechanical energy traveling through a medium. The frequency of the wave is perceived as pitch, while the amplitude is perceived as loudness.
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Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages
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Automatic User Preferences Selection of Smart Hearing Aid Using BioAid.

Hafeez Ur Rehman Siddiqui1, Adil Ali Saleem1, Muhammad Amjad Raza1

  • 1Institute of Computer Science, Khawaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan.

Sensors (Basel, Switzerland)
|October 27, 2022
PubMed
Summary

This study automates hearing aid settings using acoustic scene classification, eliminating manual adjustments for hearing-impaired individuals. The system accurately predicts optimal hearing aid tuning based on the environment, enhancing user experience.

Keywords:
BioAidDCASEhearing aidmachine learningsignal processing

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Area of Science:

  • Biomedical Engineering
  • Acoustics
  • Machine Learning

Background:

  • Hearing aid user experience is often impaired by noisy environments and manual setting adjustments.
  • Current hearing aids offer sophisticated features but require users to manually switch settings for different acoustic scenes.
  • This manual process is inconvenient, hindering active participation in daily activities for individuals with hearing loss.

Purpose of the Study:

  • To automate the BioAid assistive hearing system by integrating an acoustic scene classification algorithm.
  • To eliminate the need for manual switching of hearing aid settings based on environmental changes.
  • To enable hearing aids to automatically identify and adapt to user preferences in different acoustic environments.

Main Methods:

  • Converted the open-source BioAid algorithm to Python for integration with a scene classification module.
  • Utilized the DCASE2017 dataset for training and testing acoustic scene classification models, with random forests achieving 99.7% accuracy.
  • Developed a user preference dataset by combining clean speech with various acoustic scenes, training classifiers to predict optimal hearing aid presets and subsets (100% accuracy).

Main Results:

  • Achieved 99.7% accuracy in classifying acoustic scenes using random forests.
  • Demonstrated 100% accuracy in predicting user-selected hearing aid presets and subsets based on the classified acoustic scene.
  • Successfully developed a system that automatically tunes hearing aid parameters to match user preferences for specific environments.

Conclusions:

  • The proposed automated system effectively eliminates the need for manual hearing aid setting adjustments.
  • This technology significantly enhances the user experience for individuals with hearing loss by providing seamless adaptation to acoustic environments.
  • The study highlights the potential of AI-driven scene classification to improve hearing assistive device functionality and promote greater user independence.