Related Experiment Video
Updated: Jun 25, 2026
Modified Experimental Conditions for Noise-Induced Hearing Loss in Mice and Assessment of Hearing Function and Outer Hair Cell Damage
Published on: February 10, 2023
Estimation of equivalent noise exposure level using hearing threshold levels of a population
Jennifer B Tufts1, Paul K Weathersby, Lynne Marshall
1Department of Communication Sciences, University of Connecticut, Storrs, Connecticut 06269, USA. jennifer.tufts@uconn.edu
Objectives:
Noise-induced hearing loss (NIHL) is costly in both human and economic terms. One means of reducing NIHL is to apply engineering controls to hazardous noise sources. To trade off the cost of engineering controls against the total direct monetary costs incurred by NIHL, a means of predicting the amount of NIHL that will be incurred over the life-cycle of a hazardous noise source is necessary. A widely known algorithm for the prediction of NIHL is published in ANSI S3.44-1996. However, the algorithm inputs, noise exposure level and duration, may be difficult to determine in some cases. This paper describes the conceptual basis of an approach for using ANSI S3.44-1996 to predict hearing thresholds in a population even when noise exposure levels and durations are not precisely known, and demonstrates the initial application of this approach to a single military population.
Design:
Retrospective data were obtained on the hearing-threshold levels, demographic characteristics, and noise exposure history of 250 male U.S. Navy machinists' mates. A maximum-likelihood fitting procedure was developed in which the noise level input to the algorithm was varied in order to determine the noise level that best accounted for all of the data.
Results:
The maximum likelihood fitting produced a value for the noise level input of approximately 93 dBA, with a standard error of approximately 0.3. The low standard error virtually eliminates any estimate above 94 or below 92 dBA, and indicates that a good fit to the data was achieved.
Conclusions:
This research demonstrates the feasibility of calibrating the algorithm to an individual population, even when noise exposure level or duration is not precisely known. Future work will focus on validating and generalizing this approach so that it may be used to predict hearing-threshold levels in various populations. Such an approach may be used in calculating potential cost savings in compensable hearing loss due to the application of noise control solutions.
Related Concept Videos
00:53Sound Intensity Level
The human ear can perceive an extensive range of sound intensity, necessitating the use of the logarithmic scale to define a physical quantity—the intensity level. It is a ratio of two intensities and hence a...
01:21Testing a Claim about Mean: Unknown Population SD
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used; instead...
01:22Estimating Population Mean with Unknown Standard Deviation
William S. Gosset (1876–1937) of the Guinness...
01:26Estimating Population Standard Deviation
01:16Estimating Population Mean with Known Standard Deviation
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate + error bound)
The...
01:11Testing a Claim about Mean: Known Population SD
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...