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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 polar steered response power (P-SRP) output of the SoundCompass when in the presence of three sound sources clearly points to their bearings (45°, 115° and 250°). The frequency spectrum and intensity of each sound source produce differently shaped lobes.
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f7-sensors-14-01918: The polar steered response power (P-SRP) output of the SoundCompass when in the presence of three sound sources clearly points to their bearings (45°, 115° and 250°). The frequency spectrum and intensity of each sound source produce differently shaped lobes.

Mentions: In Equation (3), the direction, κ, is replaced with the angle, θ, for simplicity, and Figure 7 presents an example of a P-SRP produced by the SoundCompass' array when exposed to three distinct sound sources, with different bearings, spectra and intensity levels.


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 polar steered response power (P-SRP) output of the SoundCompass when in the presence of three sound sources clearly points to their bearings (45°, 115° and 250°). The frequency spectrum and intensity of each sound source produce differently shaped lobes.
© Copyright Policy
Related In: Results  -  Collection

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

f7-sensors-14-01918: The polar steered response power (P-SRP) output of the SoundCompass when in the presence of three sound sources clearly points to their bearings (45°, 115° and 250°). The frequency spectrum and intensity of each sound source produce differently shaped lobes.
Mentions: In Equation (3), the direction, κ, is replaced with the angle, θ, for simplicity, and Figure 7 presents an example of a P-SRP produced by the SoundCompass' array when exposed to three distinct sound sources, with different bearings, spectra and intensity levels.

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.