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Journal of the Acoustical Society of America / Volume 122 / Issue 6 / JASA EXPRESS LETTERS

Spatial structure of low-frequency wind noise

J. Acoust. Soc. Am. Volume 122, Issue 6, pp. EL223-EL228 (2007); (6 pages)

D. Keith Wilson1, Roy J. Greenfield1, and Michael J. White2

1U.S. Army Engineer Research and Development Center, Cold Regions Research and Engineering Laboratory, 72 Lyme Rd., Hanover, New Hampshire 03755
2U.S. Army Engineer Research and Development Center, Construction Engineering Research Laboratory, 2902 Newmark Dr., Champaign, Illinois 61822

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The distinguishing spatial properties of low-frequency microphone wind noise (turbulent pressure disturbances) are examined with a planar, 49-element array. Individual, propagating transient pressure disturbances are imaged by wavelet processing to the array data. Within a given frequency range, the wind disturbances are much smaller and less spatially coherent than sound waves. Conventional array processing techniques are particularly sensitive to wind noise when sensor separations are small compared to the acoustic wavelengths of interest.

© 2007 Acoustical Society of America

Acknowledgments

Funding was provided by the U.S. Army Basic Research In-House Laboratory Independent Research (ILIR) Program. The authors recognize the outstanding efforts of David Fisk and Douglas Punt (ERDC-CRREL) and Jefferey Mifflin (ERDC-CERL) in constructing the array and performing the field measurements. We also thank Richard Raspet (Univ. of Miss.) for many helpful discussions.

Article Outline

  1. Introduction
  2. Description of experiment and apparatus
  3. Pressure field images
  4. Wavelet spectra and spatial coherence
  5. Conclusions

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KEYWORDS and PACS

Keywords

acoustic noise, acoustic signal processing, aeroacoustics, array signal processing, atmospheric acoustics, atmospheric turbulence, microphone arrays, wavelet transforms, wind

PACS

  • 43.28.Ra

    Generation of sound by fluid flow, aerodynamic sound and turbulence

  • 43.60.Cg

    Statistical properties of signals and noise

  • 43.28.Vd

    Measurement methods and instrumentation to determine or evaluate atmospheric parameters, winds, turbulence, temperatures, and pollutants in air

ARTICLE DATA

History
Received 13 Jul 2007
Accepted 05 Aug 2007
Revised 04 Aug 2007
Digital Object Identifier
http://dx.doi.org/10.1121/1.2786608

PUBLICATION DATA

ISSN

0001-4966 (print)  

Publisher

$abstract.society.value is a Member of CrossRef Acoustical Society of America

  1. S. Morgan and R. Raspet, “Investigation of the mechanisms of low-frequency wind noise generation outdoors,” J. Acoust. Soc. Am. 92, 1180–1183 (1992)JASMAN000092000002001180000001. [ISI]
  2. H. E. Bass, R. Raspet, and J. O. Messer, “Experimental determination of wind speed and direction using a three microphone array,” J. Acoust. Soc. Am. 97, 695–696 (1995)JASMAN000097000001000695000001.
  3. F. D. Shields, “Low-frequency wind noise correlation in microphone arrays,” J. Acoust. Soc. Am. 117, 3489–3496 (2005)JASMAN000117000006003489000001. [ISI] [MEDLINE]
  4. C. Torrence and G. P. Compo, “A practical guide to wavelet analysis,” Bull. Am. Meteorol. Soc. 79, 61–78 (1998).
  5. R. Raspet, J. Webster, and K. Dillion, “Framework for wind noise studies,” J. Acoust. Soc. Am. 119, 834–843 (2006)JASMAN000119000002000834000001.
  6. Z. C. Zheng and B. K. Tan, “Reynolds number effects of flow/acoustic mechanisms in spherical windscreens,” J. Acoust. Soc. Am. 113, 161–166 (2003)JASMAN000113000001000161000001. [ISI] [MEDLINE]
  7. G. P van den Berg, “Wind-induced noise in a screened microphone,” J. Acoust. Soc. Am. 119, 824–833 (2006)JASMAN000119000002000824000001.

Figures (4) Multimedia (4) Tables (1)

Figures (click on thumbnails to view enlargements)

FIG.1
(Color online). Left: W. Fairlee, VT site, showing the microphone array and solar panels. Right: Tolono, IL site, showing the microphone array and ultrasonic anemometers.

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FIG.2
(Color online). Images of DOG1 continuous wavelet transforms (CWTs) of signals from the 7×7 array. In all images, except as noted, fully saturated green corresponds to twice the maximum of the standard deviation of the time series, and fully saturated red corresponds to the negative of that value. Top of the page is north. (a) Snapshot from CWT for Case A, 5 Hz (b) Same as (a), except 15 Hz. (c) Same as (b), except 50 Hz. (d) Snapshot from CWT for Case D, 5 Hz (e) Same as (d), except 15 Hz. (f) Same as (d), except 50 Hz. (g) Same as (a), except that the signals have been delayed to adjust for the velocity of a wind gust moving across the array. (h) Same as (a), except that the signals have been delayed to adjust for the velocity of a hypothetical sound wave moving across the array. (i) Average power (dB) in the 100 Hz wavelets over 15 min interval including Case A. The total dynamic range is 5 dB from red (lower) to green (higher).

FIG.2 Download High Resolution Image (.zip file) | Export Figure to PowerPoint

FIG.3
(Color online). Global wavelet spectra (dB re 20 μPa) corresponding to the five cases in Table 1. The analyzing wavelet was DOG4.

FIG.3 Download High Resolution Image (.zip file) | Export Figure to PowerPoint

FIG.4
(Color online). Noise suppression for various array configurations as described in the text. Shown are results for Cases A (dotted), C (dashed), and D (solid). The former two cases are predominantly wind noise, whereas the latter case includes music.

FIG.4 Download High Resolution Image (.zip file) | Export Figure to PowerPoint

Multimedia

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