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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.


Theoretical and experimental directionality graphs for the SoundCompass with a fixed source at 90° with a varying frequency from 500 till 20 kHz. The Blue line marks the 90° angle. The red dotted line marks the directionality obtained by the experiment. (a) The directionality graph for the SoundCompass according to our theoretical model; (b) the experimental directionality graph for the SoundCompass. Directionality in the lower frequencies deviates from the theoretical model.
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f18-sensors-14-01918: Theoretical and experimental directionality graphs for the SoundCompass with a fixed source at 90° with a varying frequency from 500 till 20 kHz. The Blue line marks the 90° angle. The red dotted line marks the directionality obtained by the experiment. (a) The directionality graph for the SoundCompass according to our theoretical model; (b) the experimental directionality graph for the SoundCompass. Directionality in the lower frequencies deviates from the theoretical model.

Mentions: To easily determine the directionality of the SoundCompass for every frequency in the chosen spectrum of interest, we can now plot a graph in which all frequency bands with the power for every angle is compared. Figure 18 shows a stretched out P-SRP (64 angle resolution) for each frequency band. The bearing of the sound source is 90 degrees.


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)

Theoretical and experimental directionality graphs for the SoundCompass with a fixed source at 90° with a varying frequency from 500 till 20 kHz. The Blue line marks the 90° angle. The red dotted line marks the directionality obtained by the experiment. (a) The directionality graph for the SoundCompass according to our theoretical model; (b) the experimental directionality graph for the SoundCompass. Directionality in the lower frequencies deviates from the theoretical model.
© Copyright Policy
Related In: Results  -  Collection

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

f18-sensors-14-01918: Theoretical and experimental directionality graphs for the SoundCompass with a fixed source at 90° with a varying frequency from 500 till 20 kHz. The Blue line marks the 90° angle. The red dotted line marks the directionality obtained by the experiment. (a) The directionality graph for the SoundCompass according to our theoretical model; (b) the experimental directionality graph for the SoundCompass. Directionality in the lower frequencies deviates from the theoretical model.
Mentions: To easily determine the directionality of the SoundCompass for every frequency in the chosen spectrum of interest, we can now plot a graph in which all frequency bands with the power for every angle is compared. Figure 18 shows a stretched out P-SRP (64 angle resolution) for each frequency band. The bearing of the sound source is 90 degrees.

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.