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The Measurement of Loudness

Stevens, S. S.·1955·The Journal of the Acoustical Society of America

Abstract

This paper reviews the available evidence (published and unpublished) on the relation between loudness and stimulus intensity. The evidence suggests that for the typical listener the loudness L of a 1000-cycle tone can be approximated by a power function of intensity I, of which the exponent is log102. The equation is: L = kI0.3. Intensity here is assumed to be proportional to the square of the sound pressure. In terms of sones, where 1 sone is the loudness produced by a tone at 40 db above the standard reference level, the equation for loudness L as a function of the number of decibels N becomes: logL=0.03N−1.2. Otherwise said, a loudness ratio of 2:1 is produced by a pair of stimuli that differ by 10 db, and this relation appears to hold over the entire range of audible intensities. At low levels of intensity, the loudness of white noise grows more rapidly than the loudness of a 1000-cycle tone, but above the level of approximately 50 db the two loudnesses remain more nearly proportional. The suggestion is made that for all levels greater than 50 db the loudness of continuous noises may be calculated from the equation: logL=0.03N+S, where S is a spectrum parameter to be determined empirically.

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Metadata

Authors
Stevens, S. S.
Venue
The Journal of the Acoustical Society of America
Published
1955-09-01
Volume
27
Issue
5
Pages
815-829
ISSN
  • 0001-4966 (ISSN-L)
  • 1520-8524
OpenAlex
W1989771497
Cited by
467
References
0
FWCI
17.14
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: The Measurement of Loudness. https://app.cochat.ai/papers/10.1121/1.1908048
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Stevens, S. S. (1955). The Measurement of Loudness. The Journal of the Acoustical Society of America. https://doi.org/10.1121/1.1908048

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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.