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Updated: Dec 25, 2025

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Environmental Noise Classification Using Convolutional Neural Networks with Input Transform for Hearing Aids.

Gyuseok Park1, Sangmin Lee1

  • 1Department of Electronic Engineering, Inha University, Incheon 22212, Korea.

International Journal of Environmental Research and Public Health
|April 2, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced environmental noise classification algorithm for hearing aids. Utilizing convolutional neural networks (CNNs), the algorithm achieves high accuracy in identifying various noises, improving hearing aid performance with reduced complexity.

Keywords:
convolutional neural networksdeep learningenvironmental noisehearing aidshearing loss

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Area of Science:

  • Signal Processing
  • Artificial Intelligence
  • Audiology

Background:

  • Effective noise management is crucial for hearing aid functionality.
  • Accurate environmental noise classification enhances user experience and device performance.

Purpose of the Study:

  • To develop and evaluate a novel environmental noise classification algorithm for hearing aids.
  • To leverage convolutional neural networks (CNNs) and image-based signal processing for noise identification.

Main Methods:

  • Sound signals were transformed into spectrogram images.
  • Image processing techniques including sharpening masks and median filters were applied.
  • Convolutional neural networks (CNNs) were trained and tested on ten types of environmental noise data.

Main Results:

  • The proposed algorithm achieved a maximum classification accuracy of 99.25% for a 1-second spectrogram.
  • Performance remained high even with longer spectrograms (98.73% at 8 seconds), comparable to conventional methods.
  • The algorithm demonstrated reduced computational complexity without performance degradation.

Conclusions:

  • The developed CNN-based noise classification algorithm is highly effective for hearing aid applications.
  • The method offers a computationally efficient solution for real-time noise management in hearing aids.
  • This technology has the potential to significantly improve the quality of sound for hearing aid users.