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An Automated System for Sound Localization Testing in Hearing-Impaired Listeners
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SoundCompass: a distributed MEMS microphone array-based sensor for sound source localization.

Jelmer Tiete1, Federico Domínguez2, Bruno da Silva3

  • 1Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel, Pleinlaan 2, Elsene 1050, Belgium. jelmer.tiete@etro.vub.ac.be.

Sensors (Basel, Switzerland)
|January 28, 2014
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Summary
This summary is machine-generated.

We developed SoundCompass, a low-cost sensor array, to accurately map urban noise pollution sources. This technology improves upon current methods by precisely locating sound origins, aiding public health initiatives.

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Area of Science:

  • Acoustics
  • Sensor Technology
  • Environmental Monitoring

Background:

  • Noise pollution is a significant urban environmental issue linked to adverse health effects.
  • Current noise mapping methods struggle with accuracy due to reliance on limited sensor data.
  • Accurate localization of noise sources is crucial for effective urban planning and public health.

Purpose of the Study:

  • To develop a low-cost, high-accuracy sound sensor system for urban noise pollution source localization.
  • To create a data fusion technique for enhancing noise source identification in wireless sensor networks.
  • To validate the performance of the developed system in controlled and open-field environments.

Main Methods:

  • Designed and prototyped the SoundCompass, integrating 52 MEMS microphones, an inertial measurement unit, and an FPGA.
  • Developed a data fusion algorithm to combine sensor data for precise sound source directionality and location.
  • Employed wireless sensor network principles for distributed noise monitoring.
  • Conducted live tests in an anechoic chamber and simulations in a large open field.

Main Results:

  • Achieved centimeter-level sound source localization accuracy in a 25-m2 anechoic chamber.
  • Successfully simulated the localization of up to five broadband sound sources in a 10,000-m2 open field.
  • Demonstrated the feasibility of using the SoundCompass in a wireless sensor network for noise mapping.

Conclusions:

  • The SoundCompass system offers a promising solution for accurate urban noise pollution source localization.
  • The developed data fusion technique enhances the capabilities of wireless sensor networks for environmental monitoring.
  • This technology can significantly improve the accuracy of noise mapping, supporting public health and urban planning efforts.