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Real-Time Sound Source Localization for Low-Power IoT Devices Based on Multi-Stream CNN.

Jungbeom Ko1, Hyunchul Kim2, Jungsuk Kim3

  • 1Department of Health Sciences & Technology, Gachon Advanced Institute for Health Sciences & Technology (GAIHST), Gachon University, Incheon 21936, Korea.

Sensors (Basel, Switzerland)
|June 24, 2022
PubMed
Summary

This study introduces a deep neural network for real-time sound source localization in low-power Internet of Things (IoT) devices. The model accurately identifies voice origins, enabling more intuitive human-robot interaction.

Keywords:
IoT devicedeep learningmulti-stream CNNsound source localization

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Area of Science:

  • Artificial Intelligence
  • Internet of Things (IoT)
  • Acoustics

Background:

  • Voice-activated AI enhances multitasking but is limited by immobile devices requiring physical interaction.
  • Current AI devices with cameras and displays often lack mobility, hindering seamless voice interaction.

Purpose of the Study:

  • To propose a deep neural network-based real-time sound source localization (SSL) model for low-power IoT devices.
  • To develop a prototype of the SSL model implemented on actual IoT devices.

Main Methods:

  • Utilized a deep neural network architecture processing multi-channel acoustic data.
  • Employed parallel convolutional neural network layers to analyze low-, mid-, and high-frequency delay patterns.
  • Implemented the model on a Raspberry Pi 4B for real-world performance evaluation.

Main Results:

  • Achieved 91.41% accuracy in fine voice location estimation.
  • Obtained a direction of arrival error of 7.43° even in noisy conditions.
  • Demonstrated efficient processing with 7.811 ms per 40 ms sample on the Raspberry Pi 4B.

Conclusions:

  • The proposed SSL model enables accurate and efficient voice localization on low-power IoT devices.
  • This technology can be applied to mobile robots, allowing them to react naturally to voices in complex environments.