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Related Experiment Video

Updated: Dec 13, 2025

Sound Source Localization Testing in Single-sided Deafness Following Bone Conduction Intervention
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Real-time Convolutional Neural Network based Speech Source Localization on Smartphone.

Abdullah Küçük1, Anshuman Ganguly1, Yiya Hao1

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

IEEE Access : Practical Innovations, Open Solutions
|August 6, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a real-time convolutional neural network (CNN) for speech source localization (SSL) on smartphones. The novel method achieves high accuracy and low latency, even in noisy environments, aiding individuals with hearing impairments.

Keywords:
Convolutional neural networkdirection of arrival (DOA)smartphonespeech source localization (SSL)

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Area of Science:

  • Signal Processing
  • Machine Learning
  • Acoustics

Background:

  • Speech source localization (SSL) is crucial for assistive hearing technologies.
  • Existing methods often struggle with real-time performance and noisy conditions.
  • Smartphone-based solutions require efficient and robust algorithms.

Purpose of the Study:

  • To develop a real-time CNN-based SSL system for Android smartphones.
  • To introduce a novel input feature set using STFT for improved CNN performance.
  • To evaluate the system's accuracy, latency, and robustness across different smartphone hardware.

Main Methods:

  • Utilized a convolutional neural network (CNN) architecture for SSL.
  • Employed real and imaginary parts of the short-time Fourier transform (STFT) as input features.
  • Trained the CNN model on simulated and real smartphone-recorded noisy data.
  • Performed real-time inference on Android smartphones.

Main Results:

  • Achieved high accuracy (88.83% at 0dB SNR) with low latency (14ms single-frame, 180ms multi-frame).
  • Demonstrated robustness to smartphone hardware variations, reducing the need for retraining.
  • Outperformed existing CNN-based SSL methods on the same test datasets.

Conclusions:

  • The proposed CNN-based SSL method offers an accurate and efficient solution for real-time applications.
  • The system's robustness and low latency make it suitable for deployment on diverse Android devices.
  • This technology has the potential to significantly enhance hearing for individuals with hearing disorders.