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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.


Noise is smoothed out of a probability map using a low-pass Gaussian filter. This step is mandatory to accurately locate local maxima or peaks in the map. (a) A probability map of two sound sources is presented here in three dimensions to highlight the effects of noise. (b) The probability map in (a) is smoothed out here using a Gaussian filter.
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f12-sensors-14-01918: Noise is smoothed out of a probability map using a low-pass Gaussian filter. This step is mandatory to accurately locate local maxima or peaks in the map. (a) A probability map of two sound sources is presented here in three dimensions to highlight the effects of noise. (b) The probability map in (a) is smoothed out here using a Gaussian filter.

Mentions: In [11], the authors propose spatial smoothing using image filtering techniques (for example, using a low-pass Gaussian filter) to remove noise from microphone SRP-PHAT data fusion. A low-pass two-dimensional Gaussian filter transfer function is defined as:H(rx,ry)=12πσ2e−rx2+ry22σ2where σ and the kernel size are adjustable values that can be changed depending on the scale of the map. Figure 12 illustrates the results of applying the filter, H(rx, ry), in a probability map, M, where two sound sources are present.


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)

Noise is smoothed out of a probability map using a low-pass Gaussian filter. This step is mandatory to accurately locate local maxima or peaks in the map. (a) A probability map of two sound sources is presented here in three dimensions to highlight the effects of noise. (b) The probability map in (a) is smoothed out here using a Gaussian filter.
© Copyright Policy
Related In: Results  -  Collection

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

f12-sensors-14-01918: Noise is smoothed out of a probability map using a low-pass Gaussian filter. This step is mandatory to accurately locate local maxima or peaks in the map. (a) A probability map of two sound sources is presented here in three dimensions to highlight the effects of noise. (b) The probability map in (a) is smoothed out here using a Gaussian filter.
Mentions: In [11], the authors propose spatial smoothing using image filtering techniques (for example, using a low-pass Gaussian filter) to remove noise from microphone SRP-PHAT data fusion. A low-pass two-dimensional Gaussian filter transfer function is defined as:H(rx,ry)=12πσ2e−rx2+ry22σ2where σ and the kernel size are adjustable values that can be changed depending on the scale of the map. Figure 12 illustrates the results of applying the filter, H(rx, ry), in a probability map, M, where two sound sources are present.

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.