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CONVOLUTIONAL RECURRENT NEURAL NETWORK BASED DIRECTION OF ARRIVAL ESTIMATION METHOD USING TWO MICROPHONES FOR HEARING

Abdullah Küçük1, Issa M S Panahi1

  • 1The University of Texas at Dallas Department of Electrical and Computer Engineering, 800 West Campbell Richardson, TX 75080, USA.

IEEE International Workshop on Machine Learning for Signal Processing : [Proceedings]. IEEE International Workshop on Machine Learning for Signal Processing
|May 11, 2021
PubMed
Summary

This study introduces a smartphone app using a convolutional recurrent neural network (CRNN) for accurate direction of arrival (DOA) angle estimation. This technology enhances hearing aid applications by visually indicating sound sources for users with hearing loss.

Keywords:
Speech source localizationconvolutional recurrent neural networkreal-time inferencetwo microphone DOA

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Area of Science:

  • Signal Processing
  • Machine Learning
  • Assistive Technology

Background:

  • Hearing disorders significantly impact communication.
  • Existing hearing aid technology can be improved with better sound localization.
  • Direction of Arrival (DOA) estimation is crucial for spatial audio perception.

Purpose of the Study:

  • To develop a Convolutional Recurrent Neural Network (CRNN) based DOA angle estimation method.
  • To implement this method on Android smartphones for hearing aid applications.
  • To provide a visual indication of sound source direction for hearing-impaired individuals.

Main Methods:

  • Utilized the real and imaginary parts of the Short-Time Fourier Transform (STFT) as features.
  • Implemented a CRNN architecture for DOA angle estimation.
  • Performed real-time inference using only two built-in smartphone microphones.

Main Results:

  • Achieved an accuracy of 87.33% for unseen environments.
  • Demonstrated real-time performance on Android smartphones.
  • Validated the generalization and robustness of the CRNN model through real-time implementation.

Conclusions:

  • The proposed CRNN-based DOA estimation method is effective for hearing aid applications.
  • Smartphone implementation offers a practical and accessible solution for improving hearing.
  • The method shows promise for enhancing spatial awareness in individuals with hearing loss.