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SoundCompass: a distributed MEMS microphone array-based sensor for sound source localization.

Tiete J, Domínguez F, da Silva B, Segers L, Steenhaut K, Touhafi A - Sensors (Basel) (2014)

Bottom Line: One critical application is the localization of noise pollution sources in urban environments, due to an increasing body of evidence linking noise pollution to adverse effects on human health.This article presents the SoundCompass's hardware and firmware design together with a data fusion technique that exploits the sensing capabilities of the SoundCompass in a wireless sensor network to localize noise pollution sources.Live tests produced a sound source localization accuracy of a few centimeters in a 25-m2 anechoic chamber, while simulation results accurately located up to five broadband sound sources in a 10,000-m2 open field.

View Article: PubMed Central - PubMed

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

ABSTRACT
Sound source localization is a well-researched subject with applications ranging from localizing sniper fire in urban battlefields to cataloging wildlife in rural areas. One critical application is the localization of noise pollution sources in urban environments, due to an increasing body of evidence linking noise pollution to adverse effects on human health. Current noise mapping techniques often fail to accurately identify noise pollution sources, because they rely on the interpolation of a limited number of scattered sound sensors. Aiming to produce accurate noise pollution maps, we developed the SoundCompass, a low-cost sound sensor capable of measuring local noise levels and sound field directionality. Our first prototype is composed of a sensor array of 52 Microelectromechanical systems (MEMS) microphones, an inertial measuring unit and a low-power field-programmable gate array (FPGA). This article presents the SoundCompass's hardware and firmware design together with a data fusion technique that exploits the sensing capabilities of the SoundCompass in a wireless sensor network to localize noise pollution sources. Live tests produced a sound source localization accuracy of a few centimeters in a 25-m2 anechoic chamber, while simulation results accurately located up to five broadband sound sources in a 10,000-m2 open field.

No MeSH data available.


The FPGA structure of the SoundCompass.
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f4-sensors-14-01918: The FPGA structure of the SoundCompass.

Mentions: The main task of the SoundCompass's FPGA is to parallel process all digital audio signals coming from the 52 MEMS microphones and to produce a power value for a certain angle or direction. The data streams coming from the microphones are in a one bit-wide pulse density modulated format. This means that the relative density of the pulses corresponds with the amplitude of the analog audio signal. The PDM signal coming from the microphones is multiplexed per microphone pair; this allows us to use one signal line for two microphones. This means that the total bus width and pins used on the FPGA to interface the microphones is equal to half the amount of microphones. To demultiplex these signals, we use the first block in the FPGA structure, which is an interface block that splits the multiplexed PDM signals into 52 separate one bit data streams (Figure 4). This block runs at the same speed as the incoming PDM signal (2 MHz) and consists of 26 sub-blocks that each extract two one-bit data streams from each multiplexed microphone input signal. These sub-blocks extract the signal from the first microphone of the pair on the rising clock edge and the second one on the falling edge.


SoundCompass: a distributed MEMS microphone array-based sensor for sound source localization.

Tiete J, Domínguez F, da Silva B, Segers L, Steenhaut K, Touhafi A - Sensors (Basel) (2014)

The FPGA structure of the SoundCompass.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3958238&req=5

f4-sensors-14-01918: The FPGA structure of the SoundCompass.
Mentions: The main task of the SoundCompass's FPGA is to parallel process all digital audio signals coming from the 52 MEMS microphones and to produce a power value for a certain angle or direction. The data streams coming from the microphones are in a one bit-wide pulse density modulated format. This means that the relative density of the pulses corresponds with the amplitude of the analog audio signal. The PDM signal coming from the microphones is multiplexed per microphone pair; this allows us to use one signal line for two microphones. This means that the total bus width and pins used on the FPGA to interface the microphones is equal to half the amount of microphones. To demultiplex these signals, we use the first block in the FPGA structure, which is an interface block that splits the multiplexed PDM signals into 52 separate one bit data streams (Figure 4). This block runs at the same speed as the incoming PDM signal (2 MHz) and consists of 26 sub-blocks that each extract two one-bit data streams from each multiplexed microphone input signal. These sub-blocks extract the signal from the first microphone of the pair on the rising clock edge and the second one on the falling edge.

Bottom Line: One critical application is the localization of noise pollution sources in urban environments, due to an increasing body of evidence linking noise pollution to adverse effects on human health.This article presents the SoundCompass's hardware and firmware design together with a data fusion technique that exploits the sensing capabilities of the SoundCompass in a wireless sensor network to localize noise pollution sources.Live tests produced a sound source localization accuracy of a few centimeters in a 25-m2 anechoic chamber, while simulation results accurately located up to five broadband sound sources in a 10,000-m2 open field.

View Article: PubMed Central - PubMed

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

ABSTRACT
Sound source localization is a well-researched subject with applications ranging from localizing sniper fire in urban battlefields to cataloging wildlife in rural areas. One critical application is the localization of noise pollution sources in urban environments, due to an increasing body of evidence linking noise pollution to adverse effects on human health. Current noise mapping techniques often fail to accurately identify noise pollution sources, because they rely on the interpolation of a limited number of scattered sound sensors. Aiming to produce accurate noise pollution maps, we developed the SoundCompass, a low-cost sound sensor capable of measuring local noise levels and sound field directionality. Our first prototype is composed of a sensor array of 52 Microelectromechanical systems (MEMS) microphones, an inertial measuring unit and a low-power field-programmable gate array (FPGA). This article presents the SoundCompass's hardware and firmware design together with a data fusion technique that exploits the sensing capabilities of the SoundCompass in a wireless sensor network to localize noise pollution sources. Live tests produced a sound source localization accuracy of a few centimeters in a 25-m2 anechoic chamber, while simulation results accurately located up to five broadband sound sources in a 10,000-m2 open field.

No MeSH data available.