Effects of Time-Varying Noise on

Annoyance: A Review

U.S. DEPARTMENT OF COMMERCE

National Bureau of Standards

National Engineering Laboratory

Washington, DC 20234

October 1 981

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J.S. DEPARTMENT OF COMMERCE

igATIONAL BUREAU OF STANDARDS

IVBSIR 81-2377

EFFECTS OF TIME-VARYING NOISE ON

ANNOYANCE: A REVIEW

Simone L. Yaniv

Jay W. Bauer

Daniel R. Flynn

and

William F. Danner

U.S. DEPARTMENT OF COMMERCE

National Bureau of Standards

National Engineering Laboratory

Washington, DC 20234

October 1 981

U.S. DEPARTMENT OF COMMERCE, Malcolm Baldrige, Secretary

NATIONAL BUREAU OF STANDARDS, Ernest Ambler, Director

EFFECTS OF TIME-VARYING NOISE ON ANNOYANCE

Simone L. Yanlv, Jay W. Bauer, Daniel R. Flynn, and William F. Danner

ABSTRACT

This report summarizes the literature dealing with the adverse response of

people to time-varying noise, and Identifies both the acoustlal and non- acoustlcal factors that Influence the relationship between time-varying noise

and annoyance. An examination of the laboratory research concerned with the

functional relationship between annoyance and the temporal and acoustic parameters of noise shows the tenuousness of such relationships. The adequacy of

currently used and/or proposed rating procedures for predicting subjective

response to time-varying noise is examined. Critical gaps In current knowledge

are identified.

Key words: duration; general adverse effects of noise; intermittency

;

loudness; noise criteria; time

ill
The work presented in this report has been supported by the

Federal Highway Administration, Department of Transportation;

however, the content and conclusions included are solely the

responsibility of the National Bureau of Standards.

iv
TABLE OF CONTENTS

Page

1. Introduction 1

2. Effects of Temporal Factors on Adverse Response: Social Survey

Findings 3

2.1 Procedures Used to Derive Dose-Response Relationships from

Field Studies 3

2.2 Social Survey Data: Overall Findings 4

2.3 Reliability of Noise Exposure Indices in Predicting Community

Annoyance 9

2.4 Effects of Time of Day on Adverse Response 14

2.5 Role of Specific Noise Sources in the Generation of

Community Annoyance 15

2.6 Relationship Between Annoyance and Complaints 16

2.7 Noise-Annoyance Relationship: Intervening Variables 16

3. Effects of Temporal Factors on Adverse Response: Laboratory

Findings 18

3.1 Adverse Response to Multiple Noise Events 18

3.1.1 Relationship Between Interruption Rate and Adverse

Response 18

3.1.2 Relationship Between the Number of Events and Adverse

Response 19

3.1.3 Contribution of Noise Variability to the Adverse

Response to Time-Varying Noise 21

3.1.4 Adverse Response to Combined Aircraft and Traffic

Noise Sources 25

3.1.5 Speech Interference as an Intervening Variable 26

3.1.6 Time-of-Day Effects on Annoyance and Comparisons to

Social Surveys 28

3.1.7 Summary of Multi-Event Laboratory Findings 28

3.2 Adverse Response to Discrete Noise Events 29

4. Unification of Noise Indices: Various Efforts 35

4.1 General Descriptors for Environmental Noise 37

4.2 Differences in Predicted Annoyance as a Function of the

Environmental Noise Descriptor Used 40

4.3 Relationships Between Predicted and Measured Annoyance 42

5. References 45

Appendix 52

V
LIST OF FIGURES

Page

Figure 1. Mean percent of highly annoyed respondents as a function

of for eleven "clustering" social surveys 10

Figure 2. Rating curves for the perception of noise fluctuations

for fluctuation frequencies between .01 and 10 Hz, as proposed by Bennerhult, et al 41

LIST OF TABLES

Page

Table 1. Dose-Response Relationships Obtained in Various Social

Surveys 6

Table 2. Examples of Correlation Coefficients Observed Between Noise

Measures and Average Adverse Response 11

Table 3. Comparison of Laboratory Studies Designed to Assess the Role

of Noise Variability on General Adverse Response 22

Table 4. Summary of Studies on the Time-Intensity Trade-off for

Discrete Noise Events 30

Table 5. Examples of Environmental Noise Descriptors Currently Used

in Several Countries 36

Table 6. Correlation Coefficients Observed Between Measured and

Predicted Average General Adverse Response to Noise as a Function of Noise Descriptor 44

vi
1 . INTRODUCTION

This report is a survey of the literature concerning the adverse response of

people to time-varying noise. This survey was undertaken at the request of

the Federal Highway Administration (FHWA) as part of a larger research program

having the following main objectives:

o to Identify and quantify Important physical parameters which affect

human response to time-varying traffic noise associated with varying

densities of both free-flowing highway traffic and stop-and-go traffic;

o to investigate and compare various measures and computational

procedures for rating time-varying traffic noise and to investigate

which method (or methods) best predicts the adverse response of people

to the noise from various types of traffic situations;

o to develop, if necessary, Improved procedures for rating time-varying

traffic noise in terms of measurable parameters of the noise; and

o to formulate procedures by which the most useful of the above rating

procedures may be related to other commonly used environmental noise

descriptors

.

The FHWA is interested in the effects of time-varying noise because social

surveys have consistently shown that "of all the impacts of highways, [timevarying] noise from highways and urban traffic disturbs the public most" [1]*.

Indeed, recognition of the annoyance to the public caused by traffic noise has

led Congress to direct the Secretary of Transportation, through an amendment to

Title 23 of the United States Code, Section 109(i), to promulgate highway noise

standards compatible with different land uses. Based on this directive the

Federal Highway Administration promulgated highway design noise levels in 1973,

initially as Policy and Procedure Memorandum 90-2, and revised in 1976 as Federal Highway Manual, Volume 7, Chapter 7, Section 3.

During development of its highway noise standards, the FHWA considered the

environmental, social and economic impacts of various design noise levels.

Hearing impairment and general adverse response were considered to be the most

relevant human responses to noise. Based upon the then available data, the

FHWA judged that in most instances community exposures to traffic noise were

not severe enough to induce hearing impairment. Adverse response was considered to result from a combination of several factors such as speech interference, sleep interference, the desire for a quiet environment, and the inability

to satisfactorily use the telephone, TV, and radio. Insufficient information

existed at that time concerning the effect of time-varying noise on adverse

response. Therefore, for design purposes, speech interference from steadystate noise became the basis for selecting noise criteria.

* Figures in brackets indicate the literature references in section 5 of this

report

.

1
When these highway noise design criteria were promulgated, the FHWA recognized

that speed! interference caused by steady-state noise was not a sufficient

basis for standards applying to predominantly time-varying noise. Therefore,

the FHWA committed itself to reevaluating its noise standards as research data

on the effects of time-varying noise became available. The work reported here

is designed to provide the FHWA with a data base upon which to reevaluate its

noise criteria.

The first part of the present report (sec. 2) is an examination of the

findings of social surveys on the adverse response of people to time-varying

environmental noises. The scope and nature of the problems with time- varying

noise are defined and some of the factors involved in producing the adverse

response are identified in this part of the report. One factor not examined

in the present report is the effect of different frequencies of the timevarying noise. There appears to be a consensus among researchers that use of

A-weighted sound levels for measuring time-varying noises is adequate. Therefore, all levels discussed in the present report are A-weighted unless otherwise specified. In the next section (sec, 3) the findings of laboratory

studies on the effects of temporal factors on the adverse response to timevarying noise are presented. The temporal factors investigated in laboratory

experiments, typically, are those that were identified as contributing to the

adverse response in the social surveys. The final section (sec. 4) is an examination of the various noise indices devised to predict human response to

time-varying noise from measurements of the physical parameters of such noise.

It also contains a survey of several laboratory studies that attempted to assess the ability of different noise indices to predict adverse response.

2
2 . EFFECTS OF TEMPORAL FACTORS ON ADVERSE RESPONSE: SOCIAL SURVEY FINDINGS

Over the past 30 years, noise levels in residential communities have increased

in most industrialized nations. This increased environmental noise has given

rise to increased public concern, and an awareness by public officials of a need to curb community noise. However, the success of any noise abatement and

control program requires understanding the community response to environmental

noises. Since the early 1950*s, a number of investigations combining social

surveys and physical noise measurements have been conducted in several countries, in an effort to assess the effects of environmental noise in residential

areas

.

Basically, there are two approaches to assessing community response to environmental noise. The first is to examine the actions, such as complaints to

officials or law suits, taken by individuals or groups of individuals against

identifiable noise sources. The second approach is to obtain the individual

responses of impacted residents by Interviews in social surveys. Once such

data have been acquired, relationships between the physical parameters of the

noise and people's response may be sought.

Although all such field investigations have had a similar goal — to arrive at

a methodology for relating human response to the physical attributes of the

noise — diverse methods have evolved to express such a relationship. These

include, for example. Community Noise Exposure Level, Noise Exposure Forecast,

Community Noise Rating, Noise Pollution Level, Noise and Number Index, and

Traffic Noise Index, to name just a few.* In this section an attempt is made

to summarize the major findings of community noise surveys conducted in this

country and abroad, to identify the relationships between noise ratings and

human response, and to examine why so many ratings have been developed.

2.1 Procedures Used to Derive Dose-Response Relationships from Field Studies

Most social surveys have resulted from public expressions of dissatisfaction

with either the introduction of a new noise source or system in the environment or an increase in the noise produced by an existing system. Accordingly,

most social surveys were designed to assess the annoyance and community

response produced by a specific noise system, such as an airport or an urban

freeway.

One of the most conspicuous aspects of environmental noise is that it varies

greatly in magnitude and character, both among various locations within a community and, at any given location, with the time of day. Essentially, the

approach has been to sample the noise levels, throughout the day or on a continuous basis, at several locations throughout the community. In addition,

other characteristics that describe the noise system are usually recorded.

In the case of traffic noise, for example, traffic volume, traffic mix, speed,

and other variables are typically recorded.

* Descriptions and references for all noise-rating schemes discussed in this

report can be found in the appendix of the present report.

3
In order to assess the response of individuals to noise, social surveys are

used. Residents are selected and Interviewed at locations having exposure

to different noise levels. Two general approaches have been used in social

surveys. The first consists of asking people direct questions about noise:

how often they experience it, how it affects them, and how they feel about

it. The second is an indirect approach in which questions about noise are

hidden among questions relating to other aspects of the neighborhood such as safety, crime, schools, access, air pollution. Although the first approach is

straightforward and easy to implement, it has the disadvantage that people may

attempt to reply as they think the interviewer expects them to answer. An

indirect approach partially alleviates this problem. It has the added advantage of allowing the investigators not only to assess the noise impact, but

also to assess the importance of the noise problem relative to other community concerns

.

Different methods of scaling annoyance also have been used. For example, with

the method of category scaling, people may be asked which of several descriptors (e.g., "not annoyed, a little annoyed, annoyed, very annoyed") best

describes their feelings about noise in general or about a particular noise

source. Alternatively, people may be asked to give a numerical rating to the

annoyance caused by the general noise environment in which they live, or to

rate the noise from a given source on a thermometer type scale. This approach

is called numerical scaling. This type of scaling typically anchors the two

end points with a pair of verbal descriptors, such as "not annoying" and

"extremely annoying."

Regardless of which method of scaling is used, the number of categories or the

range of numbers available to the respondents may vary greatly depending upon

the particular preferences of specific investigators. For example, some investigators [2,3] prefer to use a five-point scale, while others argue [4-11] that

a seven-point scale is best, and still others prefer either a nine-point scale

[12] or an eleven-point scale [13-16]. Thus, the type of information gathered

through social surveys varies from study to study, and the annoyance scales

derived from such studies are not readily comparable.

In a specific social survey, once the physical and subjective data are gathered, the physical data base may be transformed to yield a noise descriptor

that correlates well with the subjective data. Since many investigations were designed to assess the noise from a specific noise source or system, and since

little uniformity exists as to how data are obtained and Interpreted, it is

not surprising that a plethora of indices currently exists to characterize

environmental noise.

2 .2 Social Survey Data; Overall Findings

A common result of social surveys is that people exposed to noise in their

homes show a generalized adverse response which increases with increasing noise

level. This general adverse response is complex and involves a combination of

factors. Including speech interference, sleep Interference, a frustrated desire

for quiet, and the inability to use telephone, radio, and TV satisfactorily

4
[17-22]. Thus, it appears that the major factor contributing to the adverse

response to environmental noise is frequently the activity interference produced by the noise [2,5,17,23,24].

The investigations reviewed in the course of the present study indicate that,

in the aggregate, the average response of groups of Individuals can be predicted and is correlated well with a number of different measures of cumulative

noise exposures [23,25,26]. In fact, the various cumulative noise indices that

have evolved from social surveys are highly correlated among themselves, with

correlation coefficients often greater than 0.9 [23,25]. These high correlations occur, in large part, because all of the ratings rise with Increase in

sound pressure level.

While the average response of groups of people is predictable, individual

responses vary greatly. Correlation coefficients between noise exposure and

individual annoyance scores are typically lower than 0.4 [6-8,27,28], although

Bradley [21,22] has reported correlation coefficients of 0.5. Similar results

have been observed in laboratory studies, as will be seen in the next section.

For example, Borsky [29], in a laboratory study of aircraft noise, noted a cor- relation coefficient of 0.5 between noise exposure and individual annoyance

scores, while a measure of group annoyance yielded a correlation coefficient of

0.9 or better. Griffiths and Delauzun [11] reported correlation coefficients

of 0.6 for retests of dissatisfaction with traffic noise, which seem to be the

upper limit for what can be expected for correlations of individual annoyance

to noise exposures.

Two very different types of dose-response relationships can be derived from

social surveys. These can differ significantly in terms of their usefulness.

In one type of dose- response relationship, the percent of people experiencing

a particular degree of annoyance is related to a measure of the noise exposure.

In the second type, the median (or mean) annoyance score is expressed in terms

of a measure of the noise exposure. To assess the impact of a proposed action,

it is often desirable to know the extensity , or number of people affected, as well as the Intensity , or severity of the reaction. The first type of doseresponse relationship allows for predictions of both extensity and intensity

[25]. The second type yields data only for predicting the intensity or the

"average" annoyance of the population for a given level of exposure. Noise

abatement and control programs are often based upon a philosophy of satisfying

most people most of the time. For this purpose a dose-response relationship

which specifies the extensity of annoyance for a specific level of exposure is

necessary. For research purposes, it is also necessary to have Information

about the Intensity of the annoyance response. Accordingly, it is desirable

that both types of dose-response relations be included in reports of social

surveys.

Table 1 summarizes the dose-response relationships derived in some of the major

social surveys in which the responses obtained in the survey were expressed in

terms of the percent of people annoyed. Also contained in table 1 is a statement about the noise system studied, the descriptor used to characterize the

noise, and the type of responses required from those interviewed.

5
Table

1.

Dose-Response

Relationships

Obtained

in

Various

Social

Surveys.

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Table

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There appears to be a fair amount of agreement across studies in spite of the

fact that little uniformity exists among studies regarding the methods used io

measure either the physical attributes of the noise or the subjective response

The data of table 1 agree in general with a recent finding of Schultz [23],

reproduced here in figure 1. In the Schultz study, data were examined from

18 social surveys, conducted in 9 countries, and dealing with several types

of noise sources. The physical data from each study were converted by

Schultz into a common metric, the day-night average sound level . Annoyance scores were converted into categories of "percent highly annoyed."

These were plotted as a function of day-night average sound level. Eleven of

the eighteen surveys were similar enough to allow Schultz to derive a single

dose-response relationship (fig. 1). However, among the surveys examined

by Schultz, seven could not be fitted to the curve shown in figure 1 for reasons that remain somewhat obscure. A partial reason that the seven studies do

not agree with the other survey data may be that the subjective data in these seven surveys were reported in a manner that made estimates of the "percent

highly annoyed" difficult, if not meaningless.

2 .3 Reliability of Noise Exposure Indices in Predicting Community Annoyance

The accuracy with which a particular noise exposure index predicts community

annoyance typically has been inferred from the correlation coefficients

relating measured annoyance scores and predicted scores. A number of studies

have attempted to assess how well some of the commonly-used noise descriptors

predict community annoyance. The results of such studies are summarized in

table 2. As expected, for any given noise exposure index the range of correlation coefficients is smaller within a given study than across studies. This

finding is not surprising since these indices are computed from the measured

physical attributes of the noise. Thus, in any given study those attributes

vary less than across studies.

In order to compare the predictions of different noise descriptors, it is

necessary to establish the relationships among noise decrlptors within a sxirvey, and then to determine if these relationships are the same across surveys.

Referring to table 2, it can be seen that some generalizations about the

predictions of certain descriptors can be made. For example, the statistical

descriptors (L]^q and L5 Q) and those based on mean energy values (e.g., average

sound level, Lgq) predict human response at least as precisely as some of the

more complex sound descriptors such as the Traffic Noise Index (TNI), the

Community Noise Rating (CNR), the Noise Exposure Forecast (NEF), and the Noise

Pollution Level (NPL)

.

However, in one Instance, Griffiths and Langdon [ 8 ] found that the Traffic

Noise Index predicts annoyance better than the simple statistical descriptors.

Later survey data [4-6,20,21,35,36] have failed to confirm the superiority of

the Traffic Noise Index (TNI) and indicated that in fact TNI yielded predictions that were observably worse than those obtained using other, and in

general simpler, descriptors [6,20,36].

9
%

HIGHLY

ANNOYED

40 50 60 70 80 90

DAY-NIGHT AVERAGE SOUND LEVEL IN dB

Figure 1. Mean percent of highly annoyed respondents as a function

of for eleven "clustering" social surveys [23].

10
Table

2.

Examples

of

Correlation

Coefficients

Observed

Between

Noise

Measures

and

Average

Adverse

Response

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11
Table 2

(continued)

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12

Correlation

stated

by

authors

not

to

be

statistically

significant

at

the

.05

significance

level.
One can observe in the data reviewed in the present study a trend among the

statistical descriptors (e.g., ^50» ^90)* several studies [4-6,8,

16,21,26] correlation coefficients between predicted annoyance and measured

annoyance decrease as the statistical descriptor proceeds from a "peak" value

(L]^o) to a "background" value (Lgo)* This finding suggests that, everything

else being equal, the annoyance produced by noise Increases as either the

number of distinguishable discrete events, and/or their levels, increase.

Langdon [7] examined the responses of people to noise produced by "nonfreeflowing traffic," i.e., traffic impeded by traffic lights, intersection signals, crosswalks, and congestion. He found that in congested urban traffic

situations, conventional noise descriptors such as the average sound level

(Leq), the Noise Pollution Level (NPL), and the statistical descriptors (Lio>

L50 , L9 Q) do not provide precise indications of the annoyance produced by the

noise. Low correlation coefficients — typically below 0.43 — were obtained

between predicted and measured annoyance. Moreover, Langdon *s data indicated

that in such situations the percent of heavy vehicles present in the traffic

correlates better with measured annoyance than any of the noise indices, as shown by correlation coefficients between percentage of heavy vehicles and

annoyance scores on the order of 0.7.

Bradley and Jonah [22] report that vehicle flow measures can be equal or better

predictors of human response to traffic noise than the 24-hour L^q . Further,

they note that predictors that Include vehicle flow measures as well as both

day and night L^q values improve the prediction of annoyance and activity

interference relative to predictions by the 24-hour Lgq . These findings are

in agreement with data reported by Yeowart, et al . [20], but slightly diverge

from data obtained by Vallet, et al. [18], who report that vehicle flow measures

do improve predictions of annoyance only as experienced in the evening

(10:00 p.m. - midnight) and at night (midnight - 5:00 a.m.).

If noise descriptors that account for either the number of discrete events or

the variability of noise levels over time are better predictors of community

annoyance, they should predict community response more precisely than those

that are based either on energy averages (e.g., L^^ , Q) or statistical

descriptors (e.g., L]^q> ^50)* Yet, the overall evidence from social survey

data indicates that, in most Instances, the more complex descriptors (e.g.,

NPL, TNI) do not predict annoyance better than the simpler descriptors.

The apparent contradiction discussed above remains unexplained at the present

time. A possible reason for the failure of TNI and NPL to improve the predictions of human response to noise, relative to simpler descriptors, is that both

of these Indices predict that the adverse response Increases as the variability

in noise levels Increases. However, some social survey data indicate that the

adverse response decreases as the variability in noise levels Increases [20,

21] . Moreover, both NPL and TNI only account for the range of variations in

noise levels with time, but omit other temporal parameters such as the repetition rate of discrete noise events or the rate of change of levels with

time -factors that may be important. While no direct evidence from social sur- vey data has shown that these factors have major effects on human response to

noise, laboratory studies of the adverse response to multiple events indicate

13
that these factors can affect the adverse human response to noise [27,37,44].

Details of these laboratory studies are given in section 3 of the present

report

.

2.4 Effect of Time of Day on Adverse Response

Several studies have shown that noise events occurring at night are more annoying than those that occur during the day [2,9,10,13,14,18,20,21].

People's awareness of discrete noise events at night is increased as a result

of the general decrease in background noise level that accompanies the typical

decrease in both neighborhood and indoor activities at night.

Wanner, et al. [13] reported that the proportion of people "strongly annoyed"

by a given noise exposure doubles at night. Similarly, Buchta and Kastka [14]

found that to produce an equal-annoyance score, the night-time average sound

level (Lgq) due to traffic noise must be reduced by roughly 10 dB below the

daytime value while Bradley [21] found a 9 dB difference. These findings are

in close agreement with data reported by Fidell [2] indicating that social sur- vey respondents found noise occurring at night the most annoying; the average

sound level differential reported by Fidell for a matching annoyance score was about 8 dB. Further evidence for this effect is provided in the Wilson Report

[9], where it is stated that although aircraft traffic is reduced at night

(producing a drop of 15 to 20 in the Noise and Number Index relative to the

daytime value), people still were most annoyed in the evening and early part

of the night, and were most desirous of quiet at those times of day.

Although most of the data available suggest that noise events are more annoying

at night than during the day, there are some exceptions. The number of aircraft noises reported as disturbing was examined as a function of time of day

by Ollerhead [10], who reported that disturbances from day, evening and night

events fell into the ratio 3:10:1, respectively. This finding suggests that

people are more disturbed by aircraft noise events occurring in the evening

and during the day than by those at night. In a study of railway noise

conducted in Britain, Fields [32] found no evidence for a nighttime penalty.

The reason for this is unclear.

Both the Vallet, et al . [18] and the Ollerhead [10] studies suggest that noise

events occurring in the evening hours may have a greater Impact than those

occurring during the night. However, to date the Impact of noise occurring

during the evening, as opposed to during the night, has received only limited

attention. Whether or not a penalty for events occurring during the evening

would be appropriate is unclear. Evidence, does exist, however, indicating

that noise events that occur at night should receive Increased weight when

computing cumulative noise exposure measures. However, the magnitude of this

penalty is not well defined. At present, different penalties are used in different indices. It does appear that the 10-dB penalty used in computing the

day-night average sound level is a useful approximation. The need for an evening penalty and its magnitude has not been demonstrated yet, but, as mentioned previously, there may be a need for such an adjustment.

14
2.5 Role of Specific Noise Sources In the Generation of Community Annoyance

Two social surveys [2,5] conducted in the United States indicate that p>eople

usually can Identify the specific noise sources that annoy them. These data

reveal that traffic noise is by far the most annoying noise source in most

urban areas [2j. Studies of ”non-free-f lowing traffic" by Langdon [7] and

of traffic and tramway noise by Ry lander, et al . [30] also suggest that when

a specific noise source is identifiable it becomes the major contributor to

annoyance. In the Langdon and Ry lander studies, measures based on the relative

mix of traffic (i.e., percent of heavy vehicles and number of trains, respectively) yielded better predictions of annoyance than the usual statistical

descriptors such as L]^ and Bradley [21] reports that the logarithm of

the flow rate, of either trucks alone or of all vehicles, predicted annoyance

nearly as well as, if not better than, . These studies imply that identifiable, discrete noise events play a significant role in the annoyance reaction.

Since specific noise sources usually are identified by survey respondents,

Fidell [2] and Galloway and Jones [5] have assumed that a modest lowering in

the noise level of specific noise sources might produce a significant reduction in the annoyance produced by the overall noise environment. The suggestion has been made that it may not be necessary to lower the noise levels of

all sources by a comparable amount to produce significant Improvements [5].

It also has been suggested that annoyance can be reduced by decreasing the

noise of a specific source without significantly lowering the overall noise

level [2]. Vallet, et al. [18] found that respondents reported that vehicles

were of increasing noisiness in the following order: private cars, mopeds,

motor cycles, and trucks. Vallet, et al. also argued that lowering the noise

emissions from these specific sources would reduce annoyance.

Although people can identify which noise source annoys them most, two separate

studies by Bottom [33] and Grand jean, et al. [15] have shown that annoyance

with a specific noise system can be influenced by other noise systems. For

example, the annoyance produced by aircraft noise of a given level was found

to decrease as background traffic noise increased. Moreover, in a neighborhood

in which traffic rather than aircraft noise is the major contributor to the

overall noise level, the "general noise dissatisfaction" with the overall

noise environment is a direct function of the amount of traffic [33]. However,

as the number of aircraft and thus the aircraft noise, increases, the effect

of traffic noise on the "general noise dissatisfaction" reverses; i.e., for

high aircraft noise levels dissatisfaction decreases as traffic noise level

increases. This suggests strongly that the ratio between peak levels due to

specific events and the overall background noise level is a major contributor

to human response to noise, an idea which is compatible with the suggestions of

Fidell [2] and of Galloway and Jones [5]

.

The practical implication of these

findings is that the contrast between single events and steady background

noise is an Important factor in Itself. Accordingly, the success of any noise

abatement and control program may require an understanding of the interactions

between specific, identifiable events and background levels.

15
2 .6 Relationship Between Annoyance and Complaints

Citizens’ action against noise has taken many forms, ranging from the registration of a complaint to court action. Although the rate of complaint has been

found to reflect only partially the number of people annoyed in a community,

relationships exist among rate of reported annoyance, rate of complaints, and

environmental noise levels [15,25,27].

Fidell [2] has noted that the proportion of people annoyed is typically much

larger than that which would be predicted on the basis of complaint rate alone.

He attributes this finding to the fact that, for most of the ordinary neighborhood noises found annoying, there are no obvious persons or organizations with

which to register a complaint. When this hypothesis is combined with the finding that socio-economic factors and political cohesiveness affect the rate of

complaints much more than the rate of annoyance, it is not surprising that the

rate of complaints yields unreliable predictions of the degree of annoyance

present in a community.

Finally, complaint rates are typically Influenced by publicity. For example,

McKennell [45] found that the number of registered complaints about sonic

booms during a six-month period had no relationship with the actual number of

sonic booms that occurred. Rather, peaks in the rate of complaints correlated

highly with newspaper articles about aircraft noise.

2 .7 Noise-annoyance Relationship; Intervening Variables

In the previous discussion, the major emphasis was on the relationship between

noise exposure and annoyance. However, another important aspect of research in

this area has to do with the development of models that attempt to explain how

noise causes annoyance. For this reason the present section examines the relationship among noise, the individual sensitivity of people to noise, and other

possible causal factors.

Leonard and Borsky [47] have argued that the relationship between certain

noises and human response is modified by Intervening variables such as fear and

misfeasance. These Investigators developed their conceptual model from the

results of multiple regression and correlation analyses of questionnaire data

obtained from populations living around John F. Kennedy International Airport — an area heavily Impacted by aircraft noise. These early analyses suggested

that exposure to aircraft noise causes a fear reaction which in turn causes annoyance. However, similar analyses of data gathered from the same population

at a later date show a direct link between noise exposure and annoyance.

Analyses performed by Alexandre [48] on the Leonard and Borsky data and on earlier British survey data show that, in the case of aircraft noise, there

is a relationship between fear of crashes and annoyance; however, the nature

of this relationship could not be ascertained with any certainty. Therefore,

Alexandre concluded that the Leonard and Borsky [48] model may not be correct,

and that additional information, such as personality characteristics, would be

required to determine accurately the relationships among noise exposure, fear,

and annoyance

.

16
Both Langdon [6,7,26] and Fldell [2] asked respondents to self-rate their

sensitivity to noise. Fldell [2] found that about 25 percent of those interviewed rated themselves as being more sensitive to noise than most, and Langdon

[6,7] found that a third of his subjects placed themselves in the most sensitive group. Langdon also found that as the reported sensitivity to noise

increases the reported annoyance due to noise also increases. These studies

would support the hypothesis that a noise measure incorporating a sensitivity

adjustment might improve predicted annoyance scores.

To test the role of individual sensitivity to noise, Langdon [6,7] used multiple correlation analyses to investigate the effect of including a sensitivity

factor in a noise index derived from the physical rating of noise. The data

show that while the inclusion of a sensitivity factor does improve the correlations between actual and predicted dissatisfaction scores for individuals,

it does not improve the correlation between observed and predicted median

group scores.

Griffiths and Delauzun [11] investigated individual differences in sensitivity

to traffic noise and their relationship to dissatisfaction with noise. They

found that the reliability of respondents' self-ratings of their noise sensitivity was quite low, although statistically significant, and did account for

some of the variability in dissatisfaction for respondents exposed to the same noise level. Two personality scales (Eysenck Personality Inventory and Cattell

16 PF) were also administered and failed to demonstrate any consistent correlations with the reported dissatisfaction; nor did they relate to the selfratings of noise sensitivity. Consequently, they concluded that individual

differences in general do not account for variations in noise dissatisfaction,

although sensitivity to noise accounts for a small fraction of the variation.

The overall evidence from social surveys tends to indicate that people are most bothered by noise when it interferes with their daily activities. Fidell

[2] reports that noise-sensitive individuals, as a group, are more susceptible

to the activity interference produced by the noise, thus suggesting that the

intervening variable may be activity interference. Indeed, in many of the

social surveys examined in this report, respondents reported that noise interfered with specific activities such as face-to-face conversation; listening to

television, telephone and radio; sleep; rest; or work [2-4,6,8,9,13,23-25,45,

49,50]. These activity disturbances may, in fact, be the strongest variables

influencing the annoyance response to environmental noise.

17
3. EFFECTS OF TEMPORAL FACTORS ON ADVERSE RESPONSE: LABORATORY FINDINGS

The social survey data discussed in the previous section demonstrate the com- plexity involved in describing the relationship between time-varying noise and

human response. A large volume of data also has been collected in laboratory

studies where specific temporal factors can be investigated under controlled

conditions. Basically, two types of laboratory studies have been done. The

first type involves studies of human response to noise as that noise would be

encountered in real-life situations (e.g., near airports or highways). The

second type of study has concentrated on the effects of specific temporal parameters of discrete noise events, such as single aircraft or vehicular passbys.

In the present section the major findings of both types of studies are examined

.

3 .1 Adverse Response to Multiple Noise Events

In the 1960 ’s laboratory studies of the adverse response of people to noise

focused on parameters of single events (e.g., airplane flyovers, vehicle passbys). In the next decade the focus shifted to studies of the adverse response

caused by typical time-varying noises such as produced by streams of vehicles.

These laboratory investigations have been used to study the effects on human

response of such parameters as the number of events discernable in a background

noise, the variability in the noise levels over time, the rate of interruption,

or the Interaction of various noise sources (e.g., aircraft noise superimposed

on traffic noise). The primary purpose of many studies performed in the 1970 *s was to develop indices for predicting human response to noise from measured

physical parameters of the noise — the success of such developments is discussed in section 4 of this report. Accordingly, the validity and accuracy of

such indices will be touched upon only tangentially in the present section.

3.1.1 Relationship Between Interruption Rate and Adverse Response

Anderson and Robinson [38] examined the effect of the rate of Interruption on

the annoyance produced by the noise from a road drill. Twenty-four subjects

performed paper and pencil tasks during four 30-minute test sessions. During

each session subjects were exposed to 15 minutes of road drill noise presented

at an A-weighted level of 87 dB and at four different rates of Interruption

corresponding to one burst of 15 minutes, 3 bursts of 5 minutes, 60 bursts of

15 seconds, and 180 bursts of 5 seconds duration. The durations of the bursts were such that the average sound level in each 30-mlnute test session remained

constant for each condition. Following each session, subjects were asked to

complete a noise questionnaire as well as a semantic differential scale of

paired adjectives, from which an annoyance scale was derived. As the rate of

interruption increased from 1 burst of 15 minutes to 3 bursts of 5 minutes the

annoyance produced by the noise Increased. However, no significant change in

annoyance response was observed between Interruption rates of 3 bursts of 5

minutes and 60 bursts of 15 seconds. At the highest interruption rates (i.e.,

180 bursts of 5 seconds each), the annoyance decreased somewhat.

18
Thus, Anderson and Robinson demonstrated that at low Interruption rates

annoyance increases as the rate of Interruption increases, but at high interruption rates annoyance decreases with further Increases in the interruption

rate. What happens in between these two conditions is unclear, since Anderson

and Robinson did not explore this region.

3.1.2 Relationship Between the Number of Events and Adverse Response

Several laboratory investigations have been performed to assess how the number

of noise events present during a given interval of time affects the adverse

response to noise. A hypothesis frequently examined in such studies is that

the equal energy rule holds, i.e., equal amounts of sound energy will produce

the same subjective response. However, as will be seen below, the relationship

between adverse response and the number of events is not clearly defined at the

present time.

In a study exploring the effects of level, duration, and number of events,

Langdon, Gabriel, and Creamer [37] exposed subjects to simulated aircraft fly- overs while the subjects watched television. After each half-hour session the

subjects rated the acceptability of the flyovers. These stimuli were presented

at maximum A-weighted levels of 75 and 85 dB, had durations of 2, 4, 8, and 16

seconds, and were presented at rates of 7.5, 15, 30, and 120 flyovers per hour.

Eighty subjects participated in this experiment. These subjects were split

into two groups of 40 each. Each group was then exposed to a different experimental condition. Subjects in Group 1 were exposed to each of the four flyover

rates; in this case all flyovers were kept at the same duration for a given

subject. Subjects in Group 2, on the other hand, were exposed to all four

durations, but only one of the four flyover rates was presented to any given

subject. Equal numbers of subjects in Group 1 heard each of the four durations, while equal numbers of subjects in Group 2 heard each of the four

flyover rates.

The results of this study indicate that the relationship between acceptability

of aircraft flyovers and the number of flyovers varies with the experimental

conditions. For subjects in Group 1, the acceptability ratings approximated

predictions based upon the equal-energy rule; that is, as the number of events

doubled, the unacceptability increased by the same amount as if the noise

levels had increased by 3 dB. For subjects in Group 2, however, acceptability

ratings were independent of the number of events. Accordingly, it would appear

that caution must be exercised in accepting the 3 dB per doubling-of-events

rule.

In two recent articles [27,39], Rice reports data from experiments on the

influence of the number of aircraft takeoffs and landings on the adverse reac- tion of subjects engaged in either "quiet activities" [39] or playing bridge

[27]. The number of aircraft flyovers ranged from 4 to 64 per hour in one experiment [39] and from 4 to 15 per 25-minute session in the other experiment

[27], Stimuli used in these studies were presented at five average A-weighted

peak levels ranging from 45 to 85 dB. At the end of a session the subjects

filled out a questionnaire about the noise. From the response to these

19
questionnaires subjective scale values were calculated. Both of Rice's

studies Indicate that the number of aircraft had little effect on the subjective reaction to the noise until the number reached about 16 per hour. The

data Indicated that further increases in the number of aircraft flyovers

increased the adverse response to the noise.

The data obtained by Langdon, et al. [37] for the subjects in Group 1 in

the study reported previously and the data of Rice [27,39] at first may

appear to be discordant. However, in the Langdon, et al. study, in only one instance was the aircraft traffic volume below 15 flyovers per hour. For this case Langdon' s findings indicate a rate of annoyance above that predicted by

the equal-energy hypothesis. As the air-traffic volume is increased above 15

flyovers per hour, the ratings of the noises in Langdon's study closely fit

the 3 dB per doubllng-of-events trade-off, as do Rice's data.

The practical implication of Rice's finding is that when the number of events

is low enough, they do not appear to generate any adverse response. This value

represents therefore a threshold above which further increases in the number of

events leads to increased annoyance.

Rylander, Sjostedt, and Bjorkman [40] provide evidence that there may be

an upper boundary above which annoyance no longer Increases as the number of

events increases. In the Ry lander, et al. study [40] subjects were exposed to

noise from auto traffic for 45 minutes while reading textbooks. In each of

eight sessions different numbers of truck passbys were Included, varying from

1 to 70 trucks per session. The A-weighted average sound level in each 45-

minute session was held constant at 60 dB. At the end of each session the

subjects were asked to complete a noise questionnaire. The results showed that

the percentage of subjects annoyed increased as the number of trucks Increased

from one to four, stayed constant up to 20 trucks, and then decreased as the

number of trucks increased to 70.

The data presented by Rylander, et al. [40] do not agree with those of Rice

[27,39], who did not find an upper boundary. A possible reason for the differences between the two sets of data may be that Rice's data were obtained

in studies Involving aircraft noise, while Rylander's data were obtained

with traffic noise. Indeed, Rice [39] presents evidence that for the same A-weighted average level, traffic noise is more annoying than aircraft noise,

suggesting that human response to these two types of noises differs.

In real-life situations, as the number of events occurring in a fixed period

of time Increases, the average sound level also Increases. Consequently, the

increased annoyance associated with this increased noise exposure level may

offset the decreased annoyance reported by Rylander for noise exposures held

constant in average sound level as the number of discrete events varied between

exposures

.

Rylander, et al. [40] also report results from an experiment in which the noise

exposures lasted two hours. In this study, subjects were exposed to three different volumes of truck traffic (i.e., 6, 50, and 186 trucks/two hours), presented at a constant L^q of 60 dB. Results of this phase of the study Indicate

20
that as the number of trucks increased, the percentage of subjects annoyed

decreased (53, 37, and 20 percent annoyed). Given the non-monotonic relationship between the number of trucks present in traffic and annoyance found

in the first part of this study, and the few and widely spaced values for the

number of trucks used in the second part of the Rylander, et al. [AO] study,

the relationship between the number of trucks and annoyance for this relatively

long noise exposure cannot be adequately assessed.

In summary, there is evidence that annoyance is influenced by the number of

discernable discrete events occurring in the noise. However, the nature of

the relationship between the number of events and annoyance varies among

studies, and possibly, among types of noise sources.

3.1.3 Contribution of Noise Variability to the Adverse Response to

Time-Varying Noise

The effect of fluctuations in noise level on adverse response is not clear.

Although specific formulations have been proposed to account for the effects

of fluctuations [51-53], the experimental evidence discussed below makes it

evident that the relationship between fluctuation and annoyance is still

ill-defined

.

The studies discussed in the present section share several common features.

In all studies reviewed here subjects were exposed to either time-varying traffic [27,43,54-56] or aircraft [27,39,44] noise. All studies were performed

in a laboratory setting, in either a semi-realistic living room setting [27,39,

44,54] or an anechoic chamber [45,55,56]. Typically subjects were asked to

rate their annoyance to the noise they were exposed to at the end of each exposure. Exposures typically lasted from two to thirty minutes depending on the

particular study. One exception is worth mentioning. In a series of studies

conducted by Cermak [44,56,57], pairs of stimuli were presented for about one minute. Subjects were asked to judge, for each pair of stimuli, which member

of the pair "they would rather be exposed to"; and, independently to judge also

the relative similarity among pairs of sounds.

In most of these studies the relationship between adverse response and average

sound level, Lgq, was determined. Then the average sound level was adjusted

to account for the variability in sound level over time so as to determine

whether indices that take into account variability were better predictors of

human response than just the average sound level. The variability in sound

level over time has been expressed in several ways. These are listed in table

3 together with correlation coefficients between adverse response and L^q (when

reported)

.

As can be seen in table 3, the noise level, as expressed by Lgq, is a primary

determinant of the adverse response to noise. This is seen in high correlation

coefficients between Lgq and adverse response. The effect of the variability

in noise levels is not as clear. Shepherd [44] performed a multiple regression

analysis combining Lgq and o. However, his analyses show that the addition of

0 to the regression equations does not Improve the prediction of annoyance at

the 95 percent confidence level. Andrew and May [54] did a stepwise regression

21
Table

3.

Comparison

of

Laboratory

Studies

Designed

to

Assess

the

Role

of

Noise

Variability

on

General

Adverse

Response.

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d o •iH rH QJd o o o •d > >^ 4H O OJ z 4J O QJ QJ

cfl <3 CO

d cO cu COO QJ cU OJ CO

O QJ

d

PQ 0) o rH rH .H OJa CU 01 .H QJ CX o

Pi o Pd Z dl

d CO Pi z dld CO

d

CU

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<+-l <u XJ 4J H-) o 0) 01 4-1 4-1 cw O o 4^ o w CJ CO CCJ CO 'H •H CO •cH <u •rH d d d >4-1 4^ d 4H a o 3 CO CJ CJ 14-1 4-1 O 4-4 O d d cu CO d cU H CO d •H *H U d .H d O <3 <3 H H <3 H

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22
Table

3

(continued)

73C (U

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23
analysis including such factors as Lgq, a, sex, number of truck gear changes,

and different statistical descriptors (e.g. ^50» Lgg)* The data thus

obtained showed that 33 percent of the variation in subject responses could

be explained by Lg^ alone and that only 5 percent more variation could be

accounted for by the other variables included in the equation. Thus, Andrew

and May concluded that, since 62 percent of the variance was still unaccounted

for when all the factors listed above are taken into account and since any

determination of such variables increases the complexity of the measurement of

noise, "no appreciable improvement stems from considering terms besides Lgq."

Based on a series of preference studies for pairs of traffic sounds, Cerraak

[43,56,57] also questioned the importance of noise variability in assessing

aversion to noise. Based on his data he concluded that there are physical

parameters, other than noise level, that could account for aversion to traffic

noise, but that those "variables are of limited effectiveness and generality."

In experiments where all the stimuli were presented at a constant Lgq level,

but where the variability, as represented by a, was systematically cnanged,

use of a did not improve the prediction of the subjects' preference choices.

In one experiment, predicted preference (when a was taken into account) was significant for only 4 out of 14 subjects. In a second experiment, a failed

to improve predictions for any of the subjects. The failure of a to improve

predictions in human response led Cermak to question the necessity of incorporating noise level variability in noise indices, particularly when variability

is expressed in terms of a.

From the above-mentioned studies there is some question about the importance

of variability, as expressed by the standard deviation in noise levels, in

predicting annoyance due to noise. In experiments reported by Rice [27,39],

correlation coefficients were calculated between subjective response and physical noise measures of the form Lgq + ko, where k = 0, 0.5, 1, 2, 2.56. In

all three experiments a consistent trend was found. As k increased from 0 to

2.56, the correlation between the physical and subjective measures gradually

decreased.

Further evidence is produced by Pearsons [55] who used L^q minus L5 Q rather

than a, as a measure of variability. For traffic sounds having an average

sound level of 70 dB or higher, Pearsons reported that variability did not

affect the reported annoyance. However, for average sound levels of 55 dB or lower, the highly variable noises were less annoying than the steady sounds.

Pearsons also reported that for traffic noises of moderate levels (Lgq = 60

dB) an increase in variability tended to decrease the annoyance due to the

noise, especially in conditions where subjects were trying to understand

speech. Pearsons' findings are supported by the social survey work of Bradley

[ 21 ] and Yeowart, et al. [ 20 ].

From the above experiments it appears that the variability, per se, in noise

levels is not a major factor contributing to annoyance. However, there is some evidence that under some conditions increased variability may decrease

annoyance due to noise. Some caution must be exercised in interpreting this

finding, however, since the role of the variability in the noise levels could

24
depend upon which activity coincides with the noise exposure. For example,

while a highly variable noise exposure may decrease speech Interference, it

may Increase sleep disruption.

3.1.4 Adverse Response to Combined Aircraft and Traffic Noise Sources

In the earlier parts of this section only the adverse response to a single

noise source, either traffic or aircraft, was considered. However, in the

real world noise often results from the superposition of several noise

sources. Recently several studies [42,58-60] examined the annoyance caused

by noise exposure to combined aircraft and traffic noises.

Typically, in such studies, subjects were exposed to background traffic noises

in which aircraft flyover noises were embedded. Subjects were Instructed to

rate the annoyance or noisiness of the individual aircraft flyovers. Powell

and Rice [58] found that increasing the level of a continuous background traffic noise decreased the annoyance produced by individual aircraft flyovers.

This annoyance drop was significant and roughly equivalent to decreasing the

level of the aircraft flyovers by 5 dB, when background traffic noise levels were increased from an A-weighted level of 45 dB to 65 dB. Powell [59]

obtained similar results in a pair of experiments in which aircraft type and

noise levels were varied, as were the level and variability of the background

traffic noise. Again a reduction in annoyance, equivalent to a 5 dB decrease

in the level of the individual aircraft, was obtained by increasing the background noise level from an average sound level, L^q , of 30 to 40 dB. The

effect of the variability in traffic noise was unclear, although an Interaction

between the levels of the aircraft and traffic noises was observed. The maximum reduction in annoyance occurred when the aircraft noise level was 10 dB

above the traffic noise level; however, when background traffic noise was

high, annoyance Increased with any further Increases in noise level.

Johnston and Haasz [60] examined the interaction between traffic and aircraft

noise and included the effect of duration of the aircraft signal as a parameter. They examined the effects of aircraft flybys protruding above background traffic noise, 20, 40 or 80 percent of the time during an 11-minute

session. Six flybys were presented in each session. For the conditions when

the flyovers were protruding above the background noise levels (which increased

from a mean value of 44 to 68 dB 20 and 40 percent of the time), the decreased

annoyance was as if a 5 to 6 dB reduction in the peak level of the aircraft

flybys had occurred. No decrease in annoyance was observed for increases in

the background noise when the aircraft noises were discernable 80 percent of

the time. This latter condition approached the situation where the aircraft

signals were steady-state and the background noise was effectively masked by

the aircraft signal. Consequently, the traffic background had no effect on

the observed annoyance produced by the aircraft signals when the rate of

flyovers was very high.

In another study by Powell [42]

,

subjects rated the annoyance produced by noise

exposures lasting 15 minutes. A total of 17 noise conditions was presented.

These included exposures to aircraft and road traffic presented separately at

25
four average sound levels, ranging from 30 to 60 dB in 10 dB steps, as well as exposure to nine combinations of mixed aircraft and road traffic in which each

source was presented at average sound levels of 40, 50, or 60 dB.

The data thus obtained showed that when aircraft and road-traffic noises were presented separately, the aircraft noises were found to be more annoying than

road-traffic noise presented at the same average sound level. For the combinations of the two sources, a significant interaction was found between aircraft and road-traffic noise levels. At the lowest traffic noise level (Lgq = 40 dB) , as the aircraft noise level increased there was a slight decrease in

annoyance followed by a substantial increase as the aircraft noise level was further Increased. For the middle traffic noise level (Lgq = 50 dB), as the

aircraft average noise level Increased from 40 to 60 dB, tnere was a substantial Increase in annoyance followed by a very slight decrease. For the high

traffic noise level (L^q = 60 dB), there was a decrease in annoyance followed

by a substantial increase as the airplane noise level was increased. Accordingly, there were several conditions for which the combined noises were judged

less annoying than the aircraft noise alone. Thus, for the combined exposures, an Interaction was found between aircraft and traffic noise levels which appears

complex and is not adequately assessed by the total energy concept embodied in

^eq *

The data discussed above [42,58-60] suggest that the level of a background

traffic noise influences the annoyance produced by aircraft flyovers. Moreover, when the traffic noise level is moderate to high, the maximum reduction

in the annoyance caused by aircraft noise appears to correspond to that which

would be expected if the maximum level of the aircraft noise were reduced by

5-6 dB. Complex interactions can be observed in the adverse response of people

to combinations of noise sources. When such combinations exist, some of the

noise sources play the role of signal while others act as background noise.

The difference between the "signal" and the "background" levels is an important

element of the human response since annoyance is reduced as this difference is

decreased by either lowering the signal level or raising the background level.

However, when the source which plays the role of the signal Increases to the

point where events occur very frequently, those discernable events become the

predominant factor in the adverse response and the effect of the difference

between, say, traffic noise levels and aircraft noise levels, is greatly diminished. It should be noted that similar results have been found in social sur- veys [15,33]. In these surveys the data reported showed that annoyance due to

aircraft flyovers decreased as the level of the background traffic noise

Increased, until the frequency of aircraft flyovers became very high.

3.1.5 Speech Interference as an Intervening Variable

It has been generally argued that the adverse response to noise exposure is

actually a secondary effect brought about by the activity interference produced by noise (i.e., speech interference, sleep interference) [2,5,17-24].

Pearsons [55] has explored the relationships among annoyance, speech intelligibility, and comprehension as a function of noise exposure and variability.

In these series of experiments, conducted under laboratory conditions, subjects

26
were exposed to various levels of traffic noise while listening to either

phonetically balanced lists of words (speech Intelligibility) or connected

discourse (speech comprehension). In addition, each subject was asked to rate

the annoyance produced by the noise when presented alone and in the presence of

the speech on a five-point category scale.

The data obtained by Pearsons demonstrated that annoyance caused by traffic

noise in the presence of speech decreased as the level of the speech increased.

Further, these data demonstrated that the degree to which words could be identified in the presence of traffic noise influenced the annoyance caused by the

noise. Speech-to-noise ratios were determined by adjusting the speech levels

for three different levels of speech comprehension, defined as the percent of

words correctly identified. The three groups were; low (less than 10 percent

correctly identified), moderate (50 percent identification) and high (greater

than 95 percent correct) comprehension. The subjects then rated the noise

samples in the three different speech comprehension conditions without speech

being present. The Lgq levels of the traffic samples varied from about 45 to

85 dB. For Lgq ' s below 70 dB, the traffic samples presented with speech for

the low and moderate comprehension groups were rated more annoying than the same samples without speech present. However, the annoyance ratings for the same traffic samples in the high comprehension condition were lower than the

ratings in the no-speech condition.

In section 3.1.3, it was stated that Pearsons found that annoyance decreased as the variability in the noise levels increased, especially when subjects were

trying to understand speech. However, the study noted above indicates that

the effect of noise is dependent upon the level of the speech. At high noise

levels (above an Lgq of 70 dB) , variability had no effect, whereas at lower

noise levels, annoyance decreased as the variability in the noise increased.

Pearsons also presented evidence indicating that speech comprehension is an intervening variable between noise level variability and annoyance. For

speech-to-noise ratios corresponding to moderate speech comprehension, the

effect of variability in the noise levels was clear. However, when the variability in the noise level was low, subjects on the average answered correctly

less than one question out of three about the material presented in the noise.

When the degree of variability in the noise levels was moderate or high, subjects answered two out of the three questions correctly, on the average.

Pearsons showed further that as the number of questions correctly answered

increased, the annoyance caused by traffic noises decreased. Consequently,

it appears that the improvement in speech comprehension associated with the

increased variability in the traffic noise levels is an Intervening variable

which reduces the annoyance produced by noise.

In conclusion, Pearsons' [55] results indicate that when background noises

degrade speech comprehension, the adverse response to the noise is increased.

It also appears that the characteristics of the background noise (e.g., level

and/or variability) affect speech comprehension, which itself affects the

adverse response to the noise.

Similar data obtained by Pearsons and Bennett [61] showed that in an aircraft

noise environment, subjects found the aircraft noise more annoying when

27
instructed to rate the environment "assuming that people would want to be able

to converse in it" than when they were instructed just to rate the annoyance.

The work of Pearsons and that of Pearsons and Bennett both support the concept

that the degree to which verbal communication is successful influences the

adverse response to environmental noise. This finding is consistent with the

results from social surveys discussed in section 2 of the present report.

3.1.6 Time-of-Day Effects on Annoyance and Comparisons to Social Surveys

Powell [42] and Shepherd [44], in two recent studies, attempted to compare

laboratory- based predictions of community annoyance, as a function of time

of day, with similar predictions from social surveys as synthesized by Schultz

[23]. Subjects were asked to rate the annoyance produced by noise, presented

under laboratory conditions, as if that noise were heard in their homes during

the day, evening, or night. Both Powell [42] and Shepherd [44] found differences in the mean annoyance ratings for day, evening, and night conditions.

Powell [42] obtained equivalent annoyance ratings when the Lg„ levels for the

night condition were 5 to 10 dB less than the levels for the aay condition.

The differences were influenced by the type of noise the subjects heard, with

the day-night difference being greatest for aircraft alone, then for mixed

aircraft and traffic, and lowest for traffic alone. He also found equivalent

annoyance ratings for the evening hours at levels 2 to 6 dB less than the

daytime levels, with aircraft again causing greater annoyance.

Shepherd [44] conducted laboratory studies in which subjects were asked to assess how annoying aircraft noises would be if heard in their homes during

the day, evening, and night hours. In addition, these same aircraft noises

were rated in terms of annoyance using a 10-point numerical scaling procedure.

From the data thus obtained Shepherd concluded that projected home annoyance

judgements agreed well with community annoyance as expected from Shultz' [23]

synthesis of social survey data. That is, the relationship between noise

levels and percentage of people highly annoyed reported by Shultz and that

observed by Shepherd were in good agreement. Further, the Shepherd data suggest that a 7-12 dB nighttime penalty and a 5-7 dB evening penalty, relative

to daytime values, may be required in the derivation of cumulative noise exposure indices. However, for combined aircraft and traffic noise conditions,

Increases in the percent highly annoyed of 20 to 30 percentage points were

found, relative to the social survey data, for nearly half of the stimuli.

This suggests that communities impacted by multiple noise sources at equivalent noise levels may experience more annoyance than communities impacted by a single noise system.

3.1.7 Summary of Multi-Event Laboratory Findings

From the above discussion it is clear that the adverse response to time-varying

noise is not only dependent on the level of the noise but on other parameters

as well, particularly intermittency and number of discrete noise events. Moreover, the adverse response typically increases as the rate of intermittency

and/or number of events increases. However, variability per se does not appear

28
to increase annoyance significantly. Actually, there are suggestions that in some situations the more variable noises are less annoying than steady-state

noises

.

When the offending noises originate from two different and distinct noise

systems there are level-dependent interactions between the resulting noises

that influence annoyance. In particular, the lower level noise seems to act

as background noise that "masks" the more intense noise.

The activity of the exposed people influences annoyance. Someone trying to

understand speech may be more annoyed by low-level sounds than someone just

relaxing, and annoyance increases as speech comprehension decreases. Laboratory results also indicate that time of day is an important factor and that,

if asked how they would rate the annoyance caused by a given noise exposure

level as a function of time of day, subjects rate evening and night exposures

as more annoying than similar exposures occurring during the day.

3.2 Adverse Response to Discrete Noise Events

The experiments discussed in the present section [63-75,77] indicate that as the duration of a single noise event increases, the adverse response to that

event also increases and that the trade-off between the noise level and duration varies from study to study (see table 4). Some researchers [67-70,76]

have attempted to account for the differences observed among various studies

on the effects of duration of single noise events. These attempts are also

reported below.

Kryter and Pearsons [63] investigated the effects of temporal factors on the

noisiness of sound and reported that these effects were different from those

observed on loudness. Their conclusions were derived from psychoacoustic

data obtained on 14 subjects; these data consisted of noisiness judgments

obtained by the method of constant stimuli in which subjects compared each

experimental sound to a standard sound presented at 100 dB SPL having a duration of 4 seconds and rise and fall times of 1 second each. The experimental

stimuli were presented at four levels, and had different durations (1.5 to 12

seconds) as well as different rise and decay times (0.5 to 4 seconds). (In

most of the experiments described herein, noise duration is taken to be the

time interval over which the instantaneous weighted noise level is within a predetermined level (usually 10 dB) of its peak value.)

For the spectra, intensities, and durations used in the Kryter and Pearsons

study, changes in the rise and decay times did not significantly influence

the noisiness judgments. However, duration was found to be a significant

factor. The level of the comparison sounds judged to be as noisy as the standard sound decreased by 4.5 dB for each doubling of the duration of the comparison sound. That is, as the duration of test signal was doubled, that signal

had to be reduced by 4.5 dB in order to be perceived as having the same noisiness as the reference signal. The change in intensity necessary to maintain

equal judgments of a perception (e.g., noisiness, annoyance) as the duration

changes is referred to as the "time-intensity trade-off."

29
Table

4.

Sununary

of

Studies

on

the

TimeIntensity

Trade-off

for

Discrete

Noise

Events.

0)d

rH

d) > *

W) T3 M3 MD

03 <4-1 -O • • • • Vj 4-1 CM CO CM CM CO CO CO

01

OM 1 1 1 1 1 1 1 • > 1 "O CO

d) T3dH 1 o O o

CO CM <T o o o o NO

<4-1

d r-l v£) o\ CO rH CM <r CO o o

*H /«-s 1 1 1 1 , 1 1 1 0) 4.) CO M ccj 'w' cn rH rH O o o d V4 m o iH rH CO

nj 3 • • Pi a o

0-1 2 0^ hJ CO PM t-J CO PM

PM CO

CO CO 73 73 73

tH fO PQ «s PO Q) r> 3

0) TS T3 CO 73 PQ PQ 4-1 PQ 4-4 4-1 > 73 73 73 43 73 43 43

(U CMO PC bO 0K 00

hJ 1-^

OO

OO CO

M 40 *H PQ

*-| <T> 1-H ON CO 0) CO CU 73 3 1 1 1 1 1 1 > 1 ^ 3 o v£> O O 1 O 1 40 1 (T» vO CO <3 r>- < CO <3

4-1O CO 4J

V4

O

cu cu CO

O o CO CMO

-Q •<-) rH CM o^ CM CO CM B -Q 3 3

S3 cn

CO 1 »M 1Ml

0) 0) .H .H

CO

d CO 0 < 3

•H o (U

O CO o H CU CO 73 73 CU 73 3 .H z CO •H d CU CO CU CO O

(U •H o <U cO

N *H

N *H z

4-1U 4-4 CO o CO pcS •r4

O •H

O o 4^ MM .H a: •H W Z CO Z o

cC CCj

O 0 O 73 d> 3 .H

0) >4 IM s: cu o z (U 43 Ml 43 4J 4M

a. CJ

O 4-) 73

a 4-1 4m 4-1 4m 4m

V4U 73 •H

d 4J cO

d CO

d CO CO

H •H •Hd x: CO 0) 43 >N Ml Po Ml Ml <! < CO 13 pci CO CO CJ CO O H

NO r—1

1—1 NO CO 1—7 cn •<f 73 r—1

\o »—> d LO 73 .N <r

-73 .

73 t—1cO NO

d CM 73 1—1

TJ

d 3 d CO r>. 1.^

d 3 cc) CO CO CO cO <£> CO CO

d

4-1

d

d 4J 1—< d 4-1 Po to o

COu o o CO • (U o 4J 73 >H 73 Ml

m

0) CO CO S rH rH >> CO cu Ml -H Ml 3 d

4-1 >-l )M CO cO 4J

u

d 3 CU 3 rH v4

cO CO P4U 43 cO

d

U & O rH 43

M (U 0) •H 4-1 •H cO 3 cu

O

O o 3 O

Ui PM PM

X cu hJ S PM PQ S tM s pH Pi

30

doubling

of

duration
In a later study, Pearsons [64] reported data obtained on 18 subjects for

durations ranging from 4 to 64 seconds. Over this range of durations the level

of the comparison sound decreased by an average of 2.5 dB per doubling of duration for equal judgments of noisiness as compared to the 4.5 dB decrease per

doubling reported by Kryter and Pearsons [63] for durations between 1.5 and 12

seconds. Thus it appears that noisiness is a function of duration, but that

the trade-off between level and duration for judgments of equal noisiness may

decrease as duration increases.

By piecing together the results of the above studies [63,64], Pearsons [64]

approximated the time-intensity trade-off function for annoyance by a threeline-segment function, with each segment corresponding to a specific duration

range. For durations between 1.5 and 4 seconds, the time-intensity trade-off

function is -6 dB per doubling of duration, dropping to -3.5 d3 in the range

from 4 to 16 seconds, and to -2 dB for durations between 16 and 54 seconds.

If a regression line is fitted to the data between 4 and 64 seconds the slope

of the line is -2.7 dB per doubling of duration. This value has been used

often in support of the equal energy concept; however, this interpretation may

be questioned since the slope of the time-intensity function changes depending

upon the particular durations of experimental sti.muli included in the

calculations.

An alternative approach for combining the above data [63,64] is to perform a linear regression analysis on all the data points reported in those studies,

that is for stimuli durations from 1.5 s to 64 s. This approach yields a timeintensity trade-off of -3.4 dB per doubling of duration. The goodness-of-fit

of the data points to the regression line is strong, with the coefficient of

determination being 0.84.

Later reports by Pearsons and Bennett [65,65] present a time- intensity

trade-off value of -2.6 dB per doubling of duration for aircraft spectra lasting from 1 to 100 seconds. This value agrees well with the value of -2.7 d3

reported previously by Pearsons [64] for stimuli ranging in duration from 4 to

64 seconds. However, neither value agrees with the time- intensity trade-off

value of -3.4 dB computed on the same combined data [63,64] for durations

ranging from 1.5 s to 64 s.

In a study by Hiramatsu, Takagi, Yamamoto, and Ikeno [71], the effects of

duration and level of white noise on annoyance were examined. The twenty subjects who participated in this experiment were asked to "judge the whole perceived magnitude of the sound, for example, annoyance and unpleasantness, etc.

..." by the method of magnitude esti-mation. The signals varied over a range of

duration from 30 milliseconds to 90 seconds and over a range of levels between

60 and 90 dB. Over the range of intensity and duration examined, the results

indicated that duration effects were dependent upon level. On the average,

however, the trade-off value for equal annoyance was -3.4 dB per doubling of

duration; a value that agrees well with that computed above for the combined

data of Kryter and Pearsons [63] and Pearsons [64]

.

Little and Mabry [67] confirmed that duration has an effect on the annoyance

produced by noise. Using random noise and jet noise, durations of 1 to 34

31
seconds, and noise levels ranging from 76 dB to 100 dB SPL, their data indicated that the time-intensity trade-off value is greatly variable and ranges

from -0.6 to -3.1 dB per doubling of duration. Judgments obtained without

instructing subjects to attend to duration yielded time-intensity trade-off

values in the range of -0.6 to -1.9 dB per doubling of duration. However, if

the instructions to subjects included cues for duration, the values were typically between -2.1 and -3.1 dB per doubling of duration. The median trade-off

value reported by Little and Mabry was -2.0 dB per doubling of duration.

The effect of duration cues in the instructions given to subjects on the perceived noisiness of single events was examined also by Parry [68]. In a study

of the perceived noisiness of aircraft flyovers, two groups of subjects were

required to adjust a pair of two signals until they appeared equally noisy.

One group of subjects received instructions that included duration as a judgment parameter while the other group did not. The results of this study showed

that duration cuing had a slight effect on the noisiness judgments obtained but

this effect was not statistically significant.

In 1972, Parry and Parry [76] reviewed the status of the research on the

effects of duration on the annoyance of noise [63,64,67-69]. They concluded

that duration was not a primary factor in noisiness judgments, and that the

effects of signal duration are only observed when subjects are specifically

cued to attend to differences in signal duration. However, the experiments

of Pearsons and Bennett [65,66] showed duration effects without duration cues being given in the instructions, as did two recent experiments by McCurdy and

Powell [72,73] and McCurdy [74] on the annoyance of aircraft flyovers. In the

first experiment a factorial design was used to explore the effects of duration, level, aircraft velocity, and the presence of tones as factors in simulated flyovers [72] . In the second experiment a factorial design was used

also, with the parameters studied including duration, level, and fluctuation.

In both experiments the instructions did not cue the subjects to any of the

parameters under study, yet a clear effect of duration was observed. For the

durations used — 10, 20 and 40 s — the effect of duration on annoyance was effectively the -3 dB per doubling of duration predicted by the equal -energy

hypothesis

.

In a study of actual aircraft flyovers heard on location near an airport.

Bishop [69] reported data on the effects of duration on noisiness that diverge

from those of other investigators. In the Bishop study, a series of psychoacoustic tests was conducted in dwellings located under the approach and takeoff paths of Los Angeles International Airport. In these tests, 55 residents

were asked to judge (by the method of magnitude estimation) the noisiness of

actual aircraft flyovers. Median judgments for the noisiness of approach and

takeoff noises of essentially the same perceived noise levels were the same despite the fact that on the average the duration of an approach (10 s) is

about 6 seconds shorter than that of a takeoff (16 s).

Bishop’s data differ from the results found by other investigators. Although

on approach the acoustical signals are briefer than on takeoff, they contain

more tonal components. One could argue that the increased noisiness associated

32
with the pure tones present In approaching flyover signals may offset their

shorter durations. Hence, differences in spectral content might offset effects

due to differences In duration.

Another clue as to why Bishop’s data may differ from other data comes from a study by Roslnger, Nixon, and von Gierke [70] in which subjects were asked to

judge the annoyance of artificial sounds constructed to represent approaching

and. receding aircraft flyovers. The data obtained in this stud}^ indicated that

stimuli representing approaching aircraft were generally more annoying than

those representing receding aircraft, even though the two sets of stimuli had

the same energy content, frequency distribution, and duration. This finding was attributed to the fear which may be associated with approaching aircraft.

A similar finding was reported by Hiramatsu, Wakasa, Takagi, and Yamamoto [75],

where artificial noises that increased in level with time were found to be more

annoying than otherwise identical signals that decreased in level with time.

Thus, there appears to be a difference in the degree of annoyance produced by

noise stimuli, depending on whether they increase or decrease in level over

time. Whether this difference is due to, or augmented by, fear of an approaching object has not been tested, but it could be a further explanation for the

effects observed earlier by Bishop.

Fuller and Robinson [77] have studied the effects of long durations on annoyance. During one hour sessions, while performing a pencil and paper task,

subjects were exposed to steady-state traffic noise at a mean A-weighted level

of 85 dB for either the last 5, 15, or 30 minutes of the session, or for the

entire hour. The noises were due to a steady stream of traffic in which individual car passbys were not discernable. At the end of the session, subjects

answered direct questions about the noise and completed semantic differential

scales of adjective pairs.

The authors found that as the duration of exposure increased from 5 to 15

minutes, the adverse response decreased slightly. As the duration of exposure

increased from 15 to 30 minutes, the adverse response increased sharply. As

the duration of exposure increased further from 30 to 60 minutes, a slight

decrease in the adverse response was observed. Fuller and Robinson reported

that a non-monotonic function fits their data best and that statistically significant results were obtained only with the data derived through the use of

the direct questionnaire. For this subset of data a time-intensity trade-off

value of -3.3 dB per doubling of duration was estimated.

The time-intensity trade-off ratio reported by Fuller and Robinson [77] is

certainly within the range of values obtained for single event exposures. However, due to the high intersubject variability and the non-monotonic nature of

the function, the precision of the time-intensity trade-off estimate must be

viewed with caution. The data obtained in the Fuller and Robinson study do

indicate that the adverse response to noise is affected by the duration of

long exposures, but that the exact nature of this relationship is complex and

ill-defined.

To summarize, although there exists a substantial literature concerning the

effects of discrete events of various duration on adverse response, the data

33
do not provide definitive answers regarding the time-intensity trade-off, as seen in table 4. The slope of the time- intensity trade-off appears to be

influenced by the spectra of the signal used, the level of the stimuli, the

range of duration under consideration, instructions to subjects, and emotional

reactions (e.g., fear) to the signal. Accordingly, all that can be said with

confidence is that the duration of a noise influences the annoyance response

in such a manner that as the duration of a discrete noise event increases,

from around 1 s up to at least 100 s, its aversive quality increases.

34
4. UNIFICATION OF NOISE INDICES: VARIOUS EFFORTS

As mentioned previously, a large number of environmental noise indices has

been put forward to account for human response to time-varying noise. The

reason for this plethora of indices is that most indices currently used were derived on the basis of studies dealing with the effects on people from specific noise sources or systems. For example, the Noise and Number Index (NNI)

and the Noise Exposure Forecast (NEF) deal specifically with the problem associated with aircraft flyovers, while statistical descriptors such as the Traffic Noise Index (TNI) have been derived from social survey data on traffic noise.

The proliferation of noise indices has led to situations where, even within a single country, several indices are used. For example, in the United Kingdom

three different noise ratings are currently in use to characterize the environmental noise produced by transportation systems. To characterize traffic

noise, the level exceeded 10 percent of the time (L]^q) is used. Railway noise

is characterized by the maximum A-welghted level. Aircraft noise is described

in terms of the Noise and Number Index (NNI). At the international level the

situation is further complicated since each country actively involved in environmental noise research has developed its own system(s). Table 5 shows some

of these systems together with an indication of what noise sources they are

intended to characterize. In developing table 5, the intention was not to

produce an Inventory of the various noise descriptors, but, rather, to provide

an indication of the complexity that currently exists.

This proliferation of environmental noise indices has complicated the development of meaningful noise abatement and control programs, both at the national

and international levels. The primary reason for this is that, in the absence

of a generally agreed upon environmental noise descriptor, establishing long

range goals for reducing environmental noise is progressing slowly. Yet, such

goals are essential if noise control and abatement programs are to be both

relevant and cost-effective. For example, various industries have little guidance on how to expend limited funds for noise control research and development

programs. Such expenditures are meaningful only if there is a knowledge of

future noise abatement requirements within a country, and, for products sold

internationally, of future noise abatement requirements in other countries.

An accurate understanding of the response of people to noise will provide the

means to develop a unified metric to measure all noise exposures, in order that

various noise exposures may be pooled to arrive at the total noise exposure

experienced by people. Thus noise exposure produced by various sources could

be characterized in a consistent manner and in a way that accounts for the

integrated effects of noise on people. Then criteria of acceptibility could be

chosen, for all noise sources, that included other, non-acoustic factors (e.g.,

economic factors, feasibility, and a particular community's aspirations) in the

decision making process.

35
Table 5. Examples of Environmental Noise Descriptors

Currently Used in Several Countries.

Country | Noise Descriptor 1 Application

1 United Kingdom |

1 ^10

T

1 Traffic Noise

1

1

1 Maximum A-weighted

1

1 Railway Noise

1

1 Sound Level 1

1

1

1 NNI

1

1 Aircraft Noise

1

1 United States |

1 LlO

1

1 Highway Noise

1

1

1

1 ^dn» ^eq

1

1 General Environmental

1

1

1

1

1

1 and Highway Noise

1

1

1

1 NEF

1

1 Aircraft Noise

1

1 Switzerland I

1 L5O

1

1 Traffic Noise

1

1

1

1 NNI

1

1 Aircraft Noise

1

36
4.1 General Descriptors for Environmental Noise

A number of investigators have attempted to derive general descriptors for

rating environmental noise. These attempts are summarized below.

Many descriptors that attempt to predict the effect on people of noise exposure

are in terms of a weighted equivalent sound level. Such an approach implies

that exposure to equal equivalent sound levels over a given time period should

produce equal subjective effects. Most of these descriptors are of the form

L = K log [if (4.1)

where

L(t) = the weighted noise level at time t (typically A-weighting is used),

T = period of time over which the levels are averaged, and

K = a selected constant

In the United States, K is set equal to 10, thus yielding the average sound

level, Lgq. K is also assigned the value 10 by the International Organization

for Standardization. However, values other than 10 are embodied in indices

used in other countries. The Noise and Number Index (NNI) used in the United

Kingdom uses a value of 15 while the German Stor Index, Q, uses a value of

13.3.

The value of the constant K determines the trade-off relationship betvzeen the

level of a metric and the number of noise events that occurs within a fixed

period of time. When K = 15, the trade-off relationship between the sound

level and doubling the number of events is -4.5 dBj when K = 13.3, this trading

relationship is -4 dB; and it falls to -3 dB when K = 10,

None of the indices based upon equivalent sound levels take into account the

extent of the variation in sound levels, the rate of change in sound level,

or the intermittency of Individual events — characteristics that have been

observed to contribute to subjective response.

In an attempt to explain some of the differences observed by Griffiths and

Langdon [78] in human response to traffic noises recorded at different locations, Robinson [51] re-analyzed Griffiths and Langdon ’s data and proposed the

Noise Pollution Level (NPL) . This environmental noise rating procedure is

derived from Lgq by adding a correction term that is proportional to a measure

of the extent or the variability in sound levels over time:

NPL = Leq + ka , (4.2)

37
where

Lgq = the average sound level,

k = an empirically determined constant, and

a = the standard deviation of the sound level about the mean value, as shown in the following equation

where

(4.3)

(4.4)

Robinson argues that, unlike the Traffic Noise Index (TNI), which also takes

the range of variability of levels into account, the Noise Pollution Level

applies to a variety of environmental noises. Robinson [53] suggested using

the value k = 2.56; with this choice, NPL = Lgq + (L]^q ~ ^ Gaussian

distribution of noise levels.

In a later paper, Robinson [52] suggested modifying the Noise Pollution Level.

He pointed out that the use of a standard deviation that corresponds to a long

integration time, T, is not reasonable, since people respond differently to

short-, medium-, or long-term fluctuations in sound levels and the Noise Pollution Level does not take this into account. He suggested that a be based

primarily upon sound level fluctuations having durations of approximately one minute with fluctuations having longer and shorter durations being given less

weight. No validation for this modified version of the Noise Pollution Level

has been published.

In 1971, Muller [79] suggested that the rate of change of a time-varying signal

is an important factor in terms of human response. He pointed out that none of

the previously-existing rating schemes accounted for this parameter. Accordingly, he proposed a new descriptor based upon the equivalent sound level, plus

a correction term that is a function of the root-mean-square value of the rate

of change of sound level with time:

Lgq * ^eq ^(®*) » (4.5)

where

a*

T

(dL/dt)^dt

L *'0

1/2

(4.6)

in which dL/dt is the rate of change in the A-welghted sound level with respect

to time.

38
Muller [79] proposed that f(a') be tentatively defined as:

f(a') = 10 log (1 + 15a') , (4.7)

10

which Implies that a doubling of an already large a' will Increase f(c’) by

3dB.

More recently Matschat, Muller and Zimmermann [80] argued that, in the development of a general noise Index, both the rate of change of level with time and

the differential sensitivity of people to short-, medium-, or long-term fluctuations of the noise levels must be accounted for. Accordingly, they introduced

a new general descriptor, Lg, which is described in the appendix. A particular

case of this general descriptor Is

Lg = k log^^l^j |i+ (4.8)

where

L(t) = weighted noise level at time t (typically A-weighting is used),

T = period of time over which the levels are averaged,

k = an empirically determined constant,

= "time constant which determines the limit beyond which rates of

change of the level, dL/dt, contribute significantly to the noise

index value .

On the basis of data from an aircraft noise survey, Matschat, et al . suggest

that T* be of the order of 0.5 s. For eq. (4.8), this means that rates of

change of level on the order of 1-2 dB/s, or higher, contribute significantly

to Lg.

Bennerhult, Lundquist, Nilsson, and Voigt [81] have proposed a new noise

index — the Fluctuation Level, Lf ]^ . This general noise index is also purported

to account for the sensitivity of people to modulation of noise signals. It is

defined as follows:

where

'fl

20 log

10

P(t) + C log (FR),

10

(4.9)

p(t) = A-weighted sound pressure, as a function of time, normalized by

dividing by the reference sound pressure of 20 pPa,

T = the duration of the time sequence studied.

39
k = a constant, empirically determined,

C = a constant, empirically determined,

FR = a rating number related to the frequency-weighted Fourier transform

of the A-welghted time history.

The leading term on the right hand side of eq. (4.9) is an equivalent sound

level, and can be put in the form of eq. (4.1) by noting that |p(t)|2 = 10L(t )/10 and k = 20/K. The second term in eq. (4.9) is a correction intended

to account for amplitude fluctuation in the A-weighted time history. It is

obtained by a frequency analysis of the detected (or squared and low-pass filtered) A-weighted sound pressure and comparing the resulting curve of spectrum

level versus frequency (0.01 to 10 Hz), with a series of rating curves (see

fig. 2 ) derived from laboratory studies of human perception of amplitudemodulated signals.

The noise Indices discussed above have been developed as tools for characterizing environmental noise. They are essentially Independent of the noise

source, and are expressed in terms that are purported to be relevant to human

response

.

Matschat, et al. [80] argue that in order for a noise index to be used as

a general purpose index for time-varying noise, it must meet a specific

requirement for consistency: "the noise index for the total reference time

period remains unchanged if the noise exposure L(t) within a sub-interval of

the reference time period is replaced by an equally annoying exposure (l.e., an exposure having the same index value for that sub-interval)." The Noise

Pollution Level, Lgq ’ , and indices such as the Traffic Noise Index do not meet

the requirement postulated by Matschat, et al. On the other hand, Lgq, Lg, and

the statistical descriptors (i.e., L]^q, L5 Q, etc.) do fulfill this consistency

criterion. It may be argued, however, that the necessity for the consistency

requirement postulated by Matschat, et al. has not been established. Conceivably, the same noise presented at different times could produce different

auViJi.se responses, depending upon the preceding noise exposures and the conditions of a particular situation. If this were indeed the case, then the con- sistency requirement may not be a necessary condition.

4.2 Differences in Predicted Annoyance as a Function of the Environmental

Noise Descriptor Used

Each of the general noise ratings discussed above emphasizes different aspects

of time-varying noise. Thus the predictions of human adverse response given by

each descriptor are generally different. Lgq is based on the idea that the

adverse response increases as the average sound level Increases. Use of both

the Noise Pollution Level, NPL, and Traffic Noise Index, TNI, is based on the

assumption that the general human response to noise grows with the average

sound level, as well as with Increases in the extent of the variability in the

noise levels. However, neither Lgq, NPL, nor TNI differentiates among noises

differing in their rate of fluctuation as opposed to their extent. Independent

of whether one noise has rapid fluctuations in noise level and another has

40
Fluctuation

levs!

in

dB

(A-weighted)

FB 50

FB 20

FB to

FB 5

FR 2

FR t FR 0.5

O.Ot 0.1 Fluctuation frequency in Hz

Figure 2. Rating curves for the perception of noise fluctuations for

fluctuation frequencies between .01 and 10 Hz, as proposed

by Bennerbult, et al . [82].

41
slow fluctuations, if both noises have the same average sound level and equal

overall variability, the ratings for the two noises would be Identical if Lgq,

NPL or TNI were used to rate them.

The two time-derivative-based indices, L^q ' and Lg, were proposed with the goal

of taking the rate of change in levels into consideration. Each rating scale

increases as a function of both the rate of change in noise level and the average sound level of the noise. They differ, however, in the way in which the

rate of change is handled in the computations. Accordingly, the two indices

yield different predictions of human response and are not equally sensitive to

various rates of change in levels. The Fluctuation Level, Lf^, [81] was proposed to account for human sensitivity to amplitude modulation, but this is

dependent upon the frequency of modulation. Accordingly, different noises

will be rated differently depending upon the frequency of modulation of the

noise. Similarly, the modified NPL also differentiates among noises, depending

on the frequency at which a particular noise fluctuates.

4 .3 Relationship Between Predicted and Measured Annoyance

In several studies [27,39,41,42,54,81,84,87] both data available in the

literature and new experimental data were used to compare predictions based

on several indices (in particular, Lgq , NPL, Lgq*, Lg and Lf^) to subjective

responses measured by jury tests. A brief description of these experiments and

a comparison of the results are presented below.

Rice required subjects to listen to aircraft [39] and traffic noise [27] while

performing a task. Upon completion of the task, and following noise exposures

lasting 25 minutes to one hour, the subjects were asked to fill out a questionnaire designed to assess their feelings about the noises.

In a review article prepared by the International Organization for Standardization [82], several noise ratings computed from previously published data were

compared to subjective responses obtained in studies reported by the Society of

Automotive Engineers (SAE) [83] and by Voigt [84]. In the SAE study [83], jury

data were collected by having subjects adjust the level of a "pseudo-flyover

signal" until it was equal in annoyance to 60 seconds of a recorded aircraft

flyover. In the Voigt study [84] , subjects were exposed to 12-minute segments

of traffic noises differing in rate of fluctuation. Following each exposure,

subjects were asked to rate the annoyance of the noise. From these data an annoyance rating was derived.

Bennerhult, et al. [81] exposed subjects to the same set of nine traffic noises

used in the Voigt [84] study, and derived an annoyance scale from the subjects'

judgments. Andrew and May [54] presented traffic noises to subjects while also

presenting a taped radio program. The subjects judged the annoyance of the

traffic noises. From these data an annoyance scale was derived.

In other studies of traffic noise, Jacobsen [41] had subjects rate the annoyance caused by 45-minute samples of traffic noise and Rasmussen [85] had subjects rate 30-minute samples of simulated traffic noise. Powell [42] exposed

42
subjects to combinations of aircraft flyover and backgrounds of traffic noise.

Subjects were asked to rate the annoyance of these mixed aircraft and traffic

noises after exposures of 15 minutes.

The correlation coefficients between human response data and expected

responses, based on various noise indices explored in the studies described

above, are summarized in table 6. It is seen that Lgq had the highest correlation coefficient in five of the nine studies. The exceptions were the ISO

recalculations [82] of the SAE data [83], in which the correlation coefficient

for Lg was highest, the Bennerhult, et al. study [81], in which Lgq and Lfj were

about equally high, the Jacobsen [41] study in which Lgq and Lgq ' were equal,

and the Rasmussen [85] study where all correlations were high. In seven of the

eight relevant studies the Noise Pollution Level, NPL, had the lowest correlation coefficient, although in the aircraft studies of Rice [27,39] it still

compared favorably to Lgq, as it did in the Rasmussen [85] study of simulated

traffic. (It should be noted that the correlations reported for Lgq and NPL

in the ISO study of Voigt's data were not recalculated, but were taken directly

from Voigt's original study [84].)

The two time-derivative based indices, Lgq' and Lg, do approximately as well as Lgq and the other indices; in one case Lg had a higher correlation coefficient

than Lgq when predicting jury ratings of noise annoyance [83]. However, given

the increased complexity involved when using Lgq' or Lg , one would want to see the predictive abilities of these indices established more firmly before adopting either one for general use. The data base available for evaluating either

index is still scant. Similarly, in the one test of Lf^ [81], both Lgq and Lf]^

predicted human response very well, and little improvement appeared to be

obtained by the Increased complexity of the Lf^ index.

The relationship between laboratory-measured annoyance and predictions based

on the several indices is high as seen in table 6. However, several factors

could contribute to a better understanding of the relationship between annoyance and noise. These include defining the dose-response relationship between

annoyance and the number of discrete noise events [27,37,39,40], determining

the effect of background noises on the response to the overall noise environment [10,33,42,58-60], indicating the role of the activity of the respondent

as a modulator of annoyance [3,4,6,8,9,13,23-25,45,49,50,55,61], and determining the dose-response relationship between annoyance and the rate and

amount of fluctuation in noise level [20,21,27,39,43-45,54-57]. Research that

increases the knowledge about any of these topics could improve the ability to

predict adverse response from physical measurements of time-varying noise.

43
Table 6. Correlation Coefficients Observed Between Measured and Predicted

Average General Adverse Response to Noise as a Function of Noise

Descriptor*

.

Study i Type of NoiseSource or System

1 Noise Descriptor

I Lgq 1 NPL ^eq

1 Lb 1 Lfl

ISO-SAE 1 Aircraft 1 .79+ 1 .54+ .77+

1

1 00 +

1

! [851 1 1 1 1

ISO-Voigt 1 Traffic

1

1 .98 1 .79 .96

1

1 .95

1

1 [821 1 1 1 1

Rice 1 Aircraft

1

1 .95 1 .92

1

1

1

1 [391 1 1 1 1

Rice 1 Aircraft

1

1 .97 1 .93

1

1

1

1 [271 1 Traffic 1 .94 1 .81 — 1 — 1 —

Andrew and I Traffic

1

1 .70 1 .63

1

1

1

1 May [541 I 1 1 1

Bennerhult, | Traffic

1

1 .98 I

1

1

1

1 .99

et al. [811 1 1 1 1

Jacobsen | Traffic

1

1 .98 1 . 11 + .98

1

1 .98

1

1 ,, — . [411 1 1 1 1

Powell [42] 1 Aircraft &

1

1 .86 1 .62

1

1 „

1

1 . Traffic 1 1 1

Rasmussen | Traffic

1

1 .94 1 .97 .93

1

1 .93

1

1

_ [85] 1 1 1 1

* All correlation coefficients shown are significant at the .001

level unless otherwise stated.

Significant at the .01 level

^ Significant at the .05 level.

44
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Britain, pp. 585-590, in Proceedings of Inter-Noise 78, San Francisco,

Calif., U.S. A., W. W. Lang (ed.) (1978).

[35] Large, J. B. and Ludlow, J. E., Community reaction to noise from a construction site. Noise Control Eng. 6, 59-65 (1976).

[36] Cops, A., Myncke, H., Gambart, R. , and Steenackers, P., Traffic Noise

measurements and their relation with annoyance, pp. 605-608, in Proceedings

of Inter-Noise 78, San Francisco, Calif., U.S. A., W. W. Lang (ed.) (1978).

[37] Langdon, L. E., Gabriel, R. F., and Creamer, L. R., Judged acceptability

of noise exposure during television viewing, J. Acoust. Soc . Am. 56 , 510-515 (1974).

47
[38] Anderson, C. M. B., and Robinson, D. W., The Effect of Interruption Rate

on the Annoyance of an Intermittent Noise, NPL Acoustics Report Ac 53

(National Physical Laboratory, Teddington, England, 1971).

[39] Rice, C. G., Investigation of the trade-off effects of aircraft noise and

number, J. Sound Vib. 5^, 325-344 (1977).

[40] Ry lander, R. , Sjxastedt, E. and Bjorkman, M., Laboratory studies

on traffic noise annoyance, J. Sound Vib. 52 , 415-421 (1977).

[41] Jacobsen, T., Measurements and Assessment of Annoyance of Fluctuating

Noise, Report 24 (The Acoustics Laboratory, Technical University of

Denmark, 1978).

[42] Powell, C. A., Laboratory Study of Annoyance to Combined Airplane and RoadTraffic Noise, NASA Technical Paper 1478 (NASA Langley Research Center,

Hampton, Va., 1979).

[43] Cermak, G. W«, Exploratory laboratory studies of the relative aversiveness

of traffic sounds, J. Acoust. Soc . Am. 112-123 (1979).

[44] Shepherd, K. P., Cumulative annoyance due to multiple aircraft flyovers

with differing peak noise levels, J. Acoust. Soc. Am. S32 (1979).

[45] McKennell, A., Psycho-social factors in aircraft noise annoyance,

pp. 627-644, in Proceedings of the International Congress on Noise as a Public Health Problem, Dubrovnik, Yugoslavia, EPA Report 550/9-73-008

(U.S. Environmental Protection Agency, Washington, D.C., 1973).

[46] Ko, N. W. M., and Wong, V. L. P., Responses to road traffic noise; A

socio-economic approach, J. Sound Vib. 147-152 (1980).

[47] Leonard, S., and Borsky, P. N., A causal model for relating noise exposure,

psycho-social variables and aircraft annoyance, pp. 691-705, in Proceedings

of the International Congress on Noise as a Public Health Problem,

Dubrovnik, Yugoslavia, EPA Report 550/9-73-008 (U.S. Environmental

Protection Agency, Washington, D.C., 1973).

[48] Alexandre, A., An assessment of certain causal models used in surveys of

aircraft noise annoyance, J. Sound Vib. 119-125 (1976).

[49] Motor Vehicle Noise: Identification and Analysis of Situations

Contributing to Annoyance, BBN Report No. 2082 (Bolt Beranek and Newman

Inc., Canoga Park, Ca., 1974).

[50] Ahrlin, U., and Rylander, R., Annoyance caused by different environmental

noises, J. Sound Vib. 459-462 (1979).

[51] Robinson, D. W. , The Concept of Noise Pollution Level, NPL Aero Report Ac

38 (National Physical Laboratory, Teddington, England, 1969).

48
[52] Robinson, D. W. , Rating the total noise environment: Ideal or pragmatic

approach?, pp. 777-784, in Proceedings of the International Congress on Noise as a Public Health Problem, Dubrovnik, Yugoslavia, EPA Report 550/9-

73-008 (U.S. Environmental Protection Agency, Washington, D.C., 1973).

[53] Robinson, D. W. , An Essay in the Comparison of Environmental Noise Measures

and Prospects for a Unified System, NPL Acoustics Report Ac 59 (National

Physical Laboratory, Teddington, England, 1972).

[54] Andrew, C., and May, D. N. , A Laboratory Study of Annoyance due to Traffic

Noise and the Choice of Noise Descriptors, Report 77-AC-l (Ministry of

Transportation and Communications, Downsview, Ontario, Canada, 1977).

[55] Pearsons, K. S., The effect of time-varying traffic noise on speech

communication and annoyance. Noise Control Eng. ^0, 108-119 (1978).

[56] Cermak, G. W. , and Cornillon, P. C«, Multidimensional analyses of judgments

about traffic noise, J. Acoust. Soc. Am. 1412-1420 (1976).

[57] Cermak, G. W., Laboratory experiments on traffic noise annoyance,

pp. 124-143 in Community Noise, ASTM Special Technical Publication 692

R. J. Peppin, and C. W. , Rodman, (eds.) (American Society for Testing and

Materials, Philadelphia, Pa., 1979).

[58] Powell, C. A., and Rice, C. G., Judgments of aircraft noise in a traffic

noise background, J. Sound Vib. 39-50 (1975).

[59] Powell, C. A., Effects of Road-Traffic Background Noise on Judgments of

Individual Airplane Noises, NASA Technical Paper 1433 (NASA Langley

Research Center, Hampton, Va., 1979).

[60] Johnston, G. W., and Haasz, A. A., Traffic background level and signal

duration effects on aircraft noise judgment, J. Sound Vib. 543-560

(1979).

[61] Pearsons, K. S., and Bennett, R. L., Effects of Interior Aircraft Noise on Speech Intelligibility and Annoyance, NASA CR-145203 (NASA Langley Research

Center, Hampton, Va., 1977). (Also available from National Technical

Information Service as N77-29918.)

[62] Anon., Information on Levels of Environmental Noise Requisite to Protect

Public Health and Welfare with an Adequate Margin of Safety, EPA Report

550/9-74-004 (U.S. Environmental Protection Agency, Washington, D.C.,

1974).

[63] Kryter, K. D., and Pearsons, K. S., Some effects of spectral content and

duration on perceived noise level, J. Acoust. Soc. Am. 866-883 (1963).

[64] Pearsons, K. S., The Effects of Duration and Background Noise Level on Perceived Noisiness, FAA Technical Report No. FAA-ADS-78 (Federal Aviation

Administration, Washington, D.C., 1966).

49
[65] Pearsons, K. S., and Bennett, R. L., The Effects of Temporal and Spectral

Combinations on the Judged Noisiness of Aircraft Sounds, FAA Technical

Report No. FFA-NO-69-3 (Federal Aviation Administration, Washington, D.C.,

1969) .

[66] Pearsons, K. S., and Bennett, R. L., Effects of temporal and spectral

combinations on the judged noisiness of aircraft sounds, J. Acoust. Am. 49,

1076-1082 (1971).

[67] Little, J. W., and Mabry, J. E., Sound duration and its effect on judged

annoyance, J. Sound Vib. 247-262 (1969).

[68] Parry, H. J., The Noisiness of Some Simulated Flyover Spectra, Technical

Report No. 1612 (General Electric Company, Cincinnati, Ohio, 1967).

[69] Bishop, D. E., Judgments of the relative and absolute acceptability of

aircraft noise, J. Acoust. Soc. Am. 4^, 108-122 (1966).

[70] Rosinger, G., Nixon, C. W., and von Gierke, H. E., Quantification of the

noisiness of "approaching" and "receding" sounds, J. Acoust. Soc. Am. 48,

843-853 (1970).

[71] Hiramatsu, K., Takagi, K., Yamamoto, T., and Ikeno , J., The effect of

sound duration on annoyance, J. Acoust. Soc. Japan 32, 739-750 (1976).

[72] McCurdy, D. A. and Powell, C. A., Effects of duration on annoyance due to

aircraft flyover noise, J. Acoust. Soc. Am. 63, S70 (1978).

[73] McCurdy, D. A. and Powell, C. A., Effects of Duration and Other Noise

Characteristics on the Annoyance Caused by Aircraft Flyover Noise, NASA

Technical Paper 1386 (NASA Langley Research Center, Hampton, Va. 1979).

[74] McCurdy, D. A., Effects of Sound Level Fluctuations on Annoyance Caused by

Aircraft-Flyover Noise, NASA Technical Paper 1576 (NASA Langley Research

Center, Hampton, Va., 1979).

[75] Hiramatsu, K., Wakasa, H. , Takagi, K. and Yamamoto, T., Annoyance of

fluctuating noise (the validity of Lgq) , J. Acoust. Soc. Japan 641-649

(1978).

[76] Parry, H. J., and Parry J. K., The interpretation and meaning of laboratory

determinations of the effect of duration on the judged acceptability of

noise, J. Sound Vib. 51-57 (1972).

[77] Fuller, H. C., and Robinson, D. W. , Temporal Variables in the Assessment

of an Experimental Noise Environment, NPL Acoustics Report No. Ac 72

(National Physical Laboratory, Teddlngton, England, 1975).

[78] Griffiths, I. D., and Langdon, F. J., Subjective response to road traffic

noise, J. Sound Vib. 8, 16-32 (1968).

50
[79] Muller, J. L., Assessment of annoyance due to varying noise levels with

particular reference to aircraft noise, J. Sound Vib. 287-298 (1971).

[80] Matschat, K. , MUller, E.-A., and Zimmermann, G., On the formulation of

noise indices, Acustica 267-272 (1977).

[81] Bennerhult, 0., Lundquist, B., Nilsson, N.-A., and Voigt, P., A New Method

for Rating Fluctuating Noise, Document D12:1977. (Swedish Council for

Building Research, Stockholm, Sweden, 1977). (Also available from National

Technical Information Service as PB-275-704.)

[82] Evaluation of Fluctuating Noise for the Assessment of General Negative

Reactions, ISO/TC 43/SC 1 N 269, Report of Working Group 15 (International

Organization for Standardization, Geneva, Switzerland, 1976).

[83] Anon., Development of an Aircraft Flyover Noise Rating Scale: Report on

Test No. 6, Method of Adjusting Flybys (Society of Automotive Engineers,

Inc., New York, New York, 1971). (Currently available from the Society

of Automotive Engineers, Inc., Warrendale, Pa.)

[84] Voigt, P., Physikallsche Immlssions-groessen und subjektlve St'derung durch

Strassenverkehrslarm, Doctorate thesis (Eidgenbssischen Technischen

Hochschule, Zurich, Switzerland, 1974).

[85] Rasmussen, K. B., Annoyance from simulated road traffic noise, J. Sound Vib.

65, 203-214 (1979).

51
APPENDIX

This appendix contains a compilation of the calculation procedures for the

noise rating indices included in the main text, and is intended to assist the

reader in understanding these indices. It is not intended to be a compendium

of all published rating indices nor a complete documentation of the noise ratings

which are described.

In this appendix, L(t) is the A-weighted sound pressure level as a function

of time.

6.1. (e.g., Li, Lio> L 50 , Lgo, L 99 ) [6-1].* is the A-weighted sound level

re 20 yPa, in decibels, exceeded n percent of the time, where

n = 1, 10, 50 (6.1.1)

6.2. Traffic Noise Index (TNI) [6-2].

TNI = L 90 + 4(Lio-L9o)-30.

6.3. Average Sound Level (I-e,) [6-1]-

L

eq

= 10 log

10

10L(t)/10dt

( 6 . 2 . 1)

(6.3.1)

where T is the total time of observation and L(t) is the A-weighted sound

level at time t.

6.4. Stbr Index (Q) [6-3].

Q = 13.3 log

10

iCioL(t)/13.3,t

L -^0

where T and L(t) are as in eq. (6.3.1).

(6.4.1)

*Numbers in brackets refer to the references presented at the end of the appendix.

52
6.5.

Day-Night Average Sound Level (L^^) [6-1].

L

dn

= 10 log 1- + I7 3^ 0 (Ln+10 ) /loll

|24 2A -J (6.5.1)

where is the average sound level daytime period (i.e.,

0700-2200 hours) and L is the average sound level for the nighttime period

(i.e., 2200-0700 hours).

6 . 6

.

Noise Pollution Level (NPL) [6-4].

NPL = L + ka , (6.6.1)

eq

where L^^ is as in eq. (6.3.1), a is the standard deviation of the population

of A-weighted sound levels observed during the period of observation, and k

is an empirical constant selected to be 2.56.

6.7.

Modified Noise Pollution Level (Mod-NPL) [6-5].

NPL = L + ks , (6.7.1)

eq

where k is an empirical constant, L^^ is as in eq.

standard deviation computed from

(6.3.1), and s is a

s (L -L )2dt

P P

-j

dt

l /2

9 (6.7.2)

where T is the total observation time, t is an averaging time of the order

of 5 min.

and

L

P

t+T

L dt

P

9

L = 10 log

P

®

t+At

ioUt)/io^^

where At is an averaging time of the order of 5 s.

(6.7.3)

(6.7.4)

53
The integration in eq. (6.7.4) averages out components with fluctuation

frequencies larger than about 0.03 Hz. The expression in the curly brackets

in eq. (6.7.2) corresponds to the variance, in the values, over a period

of about 5 min; it therefore discriminates against components with fluctuation frequencies less than about 0.0005 Hz. The modified standard deviation,

s, is obtained from the average of this variance over the entire period of

observation.

where L is as in eq. (6.3.1) and f(a’) is a function of the root-meaneq

square value of dL/dt, the rate of change of sound level with time. That is.

6.8. L’ [6-6].

eq

( 6 . 8 . 1 )

.T 1 1/2

(dL/dt) ^dt ( 6 . 8 . 2 )

0

where T is the observation time.

Muller postulates that

f(a’) = A log (1 + Ba’) , (6.8.3)

10

assumes A = 10, and takes B = 15.

6.9. Lg [6-7].

0

(6.9.1)

where k is a constant, T is the time of observation, and

Pg(t) = ( g(t-x)p(T)dt , J —00 (6.9.2)

in which p(t) = and g is the Fourier transform of a weighting

function G(w).

54
For G(w) = 1, L„ = L

B eq

For G(o)) = 1 + iw3, Matschat, et al. obtain

(6.9.3)

where t* = (3/2k)log 10, "is a time constant which determines the limit

10

beyond which rates of change of the level dL/dt contribute significantly

to the noise index value," and is estimated to be about 10 to 25 s.

6.10. Fluctuation Level [6-8].

^fl (?>-)log

/ 10

ir\,kL(t)/20,^

1

o 1

+ C log (FR),

10

( 6 . 10 . 1 )

where k and C are constants and FR is the "Fluctuation Rating," obtained by

frequency analysis of the history of sound levels and comparisons of the

resultant spectrum to a family of rating curves.

If k = 2, the leading term in eq. (6.10.1) becomes the average sound

level, L

eq

Bennerhult, et al. adjusted the values of k and C to optimize the

agreement with subjective response data.

55
References for the Appendix

[6-1] Anon. , Information on Levels of Environmental Noise Requisite to Protect

Public Health and Welfare with an Adequate Margin of Safety, EPA Report

550/9-74-004 (U.S. Environmental Protection Agency, Washington, D.C., 1974)

[6-2] Griffiths, I. D. , and Langdon, F. J., Subjective response to road traffic

noise, J. Sound Vib. 16-32 (1968).

[6-3] Biirck, W. , Griitzmacher, M. , Meister, F. J., Muller, E.-A., and Matschat, K.

Fluglarm: Gutachten erstattet im Auftrag des Bundesministers fiir Gesundheitswesen (Aircraft noise: expert recommendations submitted under

commission from the German Federal Ministry for Public Health) (Federal

German Ministry of Public Health, Gottingen, West Germany, 1965).

[6-4] Robinson, D. W. , The Concept of Noise Pollution Level, NPL Aero Report

Ac 38 (National Physical Laboratory, Teddington, England, 1969).

[6-5] Robinson, D. W. , An Essay in the Comparison of Environmental Noise Measures

and Prospects for a Unified System, NPL Acoustics Report Ac 59 (National

Physical Laboratory, Teddington, England, 1972).

[6-6] Muller, J. L. , Assessment of annoyance due to varying noise levels with

particular reference to aircraft noise, J. Sound Vib. 1^, 287-298 (1971).

[6-7] Matschat, K. , Muller, E-A. , and Zimmermann, G. , On the formulation of noise

indices, Acustica 267-272 (1977).

[6-8] Bennerhult, 0., Lundquist, B. , Nilsson, N.-A., and Voigt, P., A New Method

for Rating Fluctuating Noise, Document D12:1977 (Swedish Council for

Building Research, Stockholm, Sweden, 1977). (Also available from National

Technical Information Service as PB-275-704.)

56
NBS-114A (REV. 2-8C) U.S. DEPT. OF COMM. BIBLIOGRAPHIC DATA

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REPORT NO.

NBSIR 81-2377

2. Performing Organ. Report No. 3. Publication Date

4. TITLE AND SUBTITLE

Effects of Time-Varying Noise on Annoyance:

A Review

5. AUTHOR(S) Simone L.

Daniel R.

Yaniv, Jay W. Bauer,

Flynn

William F. Danner, and

6. PERFORMING ORGANIZATION (If joint or other than NBS, see instructions)

NATIONAL BUREAU OF STANDARDS

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Washington, D.C. 20590

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Document describes a computer program; SF-185, FIPS Software Summary, is attached.

11.

ABSTRACT (A 200~word or less factual summary of most significant information, if document includes a significant

bi bl iograohy or literature survey, mention it here)

IS report summarizes the literature dealing with the adverse response of people

to time varying noise, and identifies both the acoustical and non*- acoustical

factors that influence the relationship between timer-varying noise and annoyance.

An examination of the laboratory research concerned with the functional relatioirship between annoyance and the temporal and acoustic parameters of noise shows the

tenuousness of such relationships. The adequacy of currently used and/or proposed rating procedures for predicting subjective response to time-varying noise is examined. Critical gaps in current knowledge are identified.

12. KEY WORDS (Six to twelve entries: alohabetical order: caoitalize onlv orooer names: and seoarate key words hv semirninn s\

duration; general adverse effect of noise; intermittency ; loudness; noise criteria;

time-varying noise , 13, AVAILABILITY

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