https://github.com/onolab-tmu/code_2020ieeeaccess_blinky
Code to reproduce the results in the paper "Blinkies: Open source sound-to-light conversion sensors for large-scale acoustic sensing and applications".
https://github.com/onolab-tmu/code_2020ieeeaccess_blinky
blinky light-conversion-sensors microphone-array sound
Last synced: 10 months ago
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Code to reproduce the results in the paper "Blinkies: Open source sound-to-light conversion sensors for large-scale acoustic sensing and applications".
- Host: GitHub
- URL: https://github.com/onolab-tmu/code_2020ieeeaccess_blinky
- Owner: onolab-tmu
- Created: 2020-02-03T05:41:02.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-04-01T01:07:50.000Z (about 6 years ago)
- Last Synced: 2025-06-10T19:58:06.508Z (12 months ago)
- Topics: blinky, light-conversion-sensors, microphone-array, sound
- Language: Python
- Size: 7.61 MB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Blinkies: Open source sound-to-light conversion sensors for large-scale acoustic sensing and applications
=========================================================================================================
This repository contains the code to reproduce the results in the paper *Blinkies: Open source sound-to-light conversion sensors for large-scale acoustic sensing and applications*.
Abstract
--------
We propose an open source hardware platform called Blinky, a sound-to-light conversion sensor that harvests sound power at low-rate and in a convenient manner.
Blinkies are made up of a central processing unit connected to two microphones and a few light-emitting devices, are powered by a battery, and protected by a robust enclosure.
Distributed in space and combined with a conventional video camera, they allow to practically sense sound power over a very large area without hassle.
We give a comprehensive overview of the proposed system and its potential applications.
We describe the hardware design and trade-offs made.
We provide a model for the channel between sound power measurements and signal acquired by the video camera.
Because each sensor is potentially affected by a different attenuation due to the channel, we propose a calibration procedure to restore the scale of the measurements.
The effectiveness of the calibration is validated in an experiment.
Finally, we demonstrate sound source localization using a hundred-and-one actual Blinkies in highly reverberent environment.
Authors
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* [Robin Scheibler](mailto: robin[dot]scheibler[at]ieee[dot]org)
* Nobutaka Ono