Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 294

The DOI matches its Crossref record.

Noise Reduction by Vegetation and Ground

Aylor, Donald·1972·The Journal of the Acoustical Society of America

Abstract

Transmission of random noise through dense corn, a dense hemlock plantation, an open pine stand, dense hardwood brush, and over cultivated soil was measured. The relation between attenuation and frequency in these diverse cases suggested models that permit the prediction of attenuation in any configuration of vegetation and soil. The corn crop had an excess attenuation of 6 dB/100 ft for each doubling of frequency between 500 and 4000 Hz. On the other hand, the stems of the hemlock, pine, and brush all reduced noise by only about 5 dB/100 ft at 4000 Hz. Bare ground attenuates frequencies of 200–1000 Hz, and the frequency of maximum attenuation depends on the soil permeability to air. Thus, tilling the soil reduced the frequency of peak attenuation from 700 to 350 Hz and increased maximum attenuation at 52 m from the source by nearly 80%. Furthermore, earlier conflicting reports of noise attenuation by vegetation appear reconciled if ground attenuation is taken into account. Scattering and ground attenuation are the principal factors in sound attenuation by vegetation. Both factors attenuate relatively less sound as distance from the sound source increases. Hence measurements far from the source can underestimate the effect of a narrow band of vegetation or soil.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1121/1.1912830

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 294

Metadata

Authors
Aylor, D.
Venue
The Journal of the Acoustical Society of America
Published
1972-01-01
Volume
51
Issue
1B
Pages
197-205
ISSN
  • 0001-4966 (ISSN-L)
  • 1520-8524
OpenAlex
W2020199061
Cited by
294
References
0
FWCI
22.50
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Noise Reduction by Vegetation and Ground. https://app.cochat.ai/papers/10.1121/1.1912830
The paper
Aylor, D. (1972). Noise Reduction by Vegetation and Ground. The Journal of the Acoustical Society of America. https://doi.org/10.1121/1.1912830

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.