Relative duration of quiet periods between events inﬂuences noise annoyance:a laboratory experiment with helicopter sounds
Armin Taghipour, Reto Pieren and Beat Schäffer
Empa, Swiss Federal Laboratories for Materials Science and Technology, Laboratory for Acoustics/ Noise Control,Überlandstrasse 129, 8600 Dübendorf, Switzerland, email: armin.taghipour@empa.ch

Abstract
Besides the commonly considered time-averaged noise exposure metrics such as the Lden , additional indicators(e.g., maximum level, eventfulness, or relative quiet time) might improve the prediction of noise annoyance.
An experiment was carried out on the effects of relative duration of quiet periods on annoyance reactions tohelicopter noise scenarios, with the relative duration being the ratio of total duration of quiet periods in a soundscenario to its total duration. Recorded ﬂybys were spatially reproduced with a hemispherical loudspeaker arrayin the laboratory. Ninety-second noise scenarios were prepared, containing two, three, four, or ﬁve ﬂybys.
All scenarios exhibited the same Leq of 66.5 dB(A), however, differed in the number of ﬂight events and theexposure levels of individual ﬂybys. Subjects were asked to make paired comparisons (i.e., two-alternative
forced choices) for three pairs of scenarios. In each pair, one scenario had a larger number of ﬂybys than theother; i.e., 4 vs. 2, 5 vs. 2, or 5 vs. 3. An analysis of the data of 56 subjects was carried out to investigate theeffect of the relative duration of quiet periods between events on annoyance. The results indicate that longer
quiet periods may mitigate noise annoyance.
Keywords: Noise annoyance, psychoacoustic laboratory experiment, quiet periods, helicopter noise

1

INTRODUCTION

To estimate noise impact such as annoyance, often time-averaged noise exposure metrics are used as predictors,
such as the equivalent continuous sound pressure level (Leq ), day-night level (Ldn ), and day-evening-night level(Lden ) [1, 2, 3]. This ignores the temporal structure of the noise and other acoustical characteristics over themeasurement period. Indicators such as the maximum level, source type, dominant events, relative quiet time,etc. might be additional indicators of annoyance. That is, at equal average levels, these parameters might be thedifferentiating factor, as to why one noise situation is more annoying to humans than the other. For example,
with respect to the source type, at comparable sound pressure levels (e.g., Leq or Lden ), aircraft noise seems to
be more annoying than road trafﬁc or railway noise [3, 4, 5].
Fleischer [6, 7] suggested that, quietness should be considered in noise measurement in addition to the averaged
level. Various methods of consideration of quiet periods were investigated by Finke [8], Krause [9], Guski [10,11, 12], and by Estévez-Mauriz and Forssén [13]. On the one hand, they emphasized the need for considerationof quietness in noise measurement. On the other hand, they suggested that not only the total length of quiet
periods, but also their distribution and individual durations could be important [8, 12, 13]. The hypothesis isthat longer quiet periods (in total and individually) might mitigate annoyance.
Furthermore, it was suggested that it should be differentiated between absolute quiet backgrounds containing
only low-level natural sounds and relative quiet backgrounds in the cities, whereby ventilation and heating
devices etc. could cause a minimum background level which is higher than in the nature. Thus, it should
be differentiated between quietness level in the ﬁrst case and the recovery level in the latter case, respectively[6, 7, 8, 9].
Dornic and Laaksonen [14] investigated effects of intermittency (i.e., noise interruptions or calm periods) on
annoyance in a laboratory experiment. Three kinds of white noise were used,: continuous, intermittent regular,
and intermittent irregular. It was found that intermittency reduced the perceived annoyance. However, in contrast7995

to other suggestions (e.g., [12, 13]) regularity or irregularity of the intermittency did not affect annoyance (inthe particular case of white noise stimuli) [14].
With that background, this paper investigates the effect of quietness by means of a psychoacoustic experiment.
At the same A-weighted equivalent continuous sound pressure level (LAeq ), annoyance from stimuli with different numbers and lengths of quiet periods were judged in paired comparisons.

2

DIFFERENTIATING INDICATORSA series of indicators have been used in this paper, which will be described brieﬂy as follows.
2.1 Number of events
It has been shown that the number of noise events might be an additional predictor of annoyance [15, 16, 17].
Since in this study isolated ﬂight events were mixed to prepare stimuli, the number of ﬂight events in eachscenario was directly available and was investigated as a possible indicator to predict annoyance.
2.2 Relative duration of quiet periods
Based on suggestions by Fleischer [6], Krause [9] delivered a formula to calculate the relative duration of quiet
periods as the ratio of total duration of quiet periods to total duration of a scenario. First, the total duration ofquiet periods Tquiet in a scenario is calculated as
Tquiet = ∑ ti ,(1)

whereby ti is the duration of the i-th (individual) quiet period in the scenario (e.g., in seconds). Then, therelative duration of quiet periods is calculated as
Relative duration of quiet periods (%) = 100 ·Tquiet,Tscenario(2)

whereby Tscenario is the total duration of a scenario (e.g., in seconds; i.e., the same unit as for ti and Tquiet ).
2.3 Intermittency ratio (IR)
Wunderli et al. [18] introduced intermittency ratio (IR) as a measure for the eventfulness and intermittency ofa scenario. It considers the individual dominant events which arise from the average-level background [4, 18].
Intermittency ratio is deﬁned as
IR (%) = 100 · 100.1(LAeq,T,Events −LAeq ) ,(3)
whereby LAeq,T,Events is calculated from contributions of events, the level of which exceed a given arbitrary
threshold K:(4)
K = LAeq +C[dB].
For the calculation of K, C is the dB-offset (i.e., the dB difference to LAeq ) beyond which events are considered [18]. In this paper, C = 3, as suggested by Wunderli et al. [18].
2.4 Centre of Mass Time (CMT)
Estévez-Mauriz and Forssén [13] introduced Centre of Mass Time (CMT) as an indicator for quiet periods,
which penalizes the fragmentation of quieter periods and values its clustering (i.e., longer time periods). CMT(in seconds) is calculated as∑ ti 2
CMT =
,
(5)
∑ ti
whereby ti is the duration of the i-th (individual) quiet period in the scenario (in seconds).

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3

METHOD

3.1 Listening test facility
The experiment was conducted in AuraLab, a listening test facility of the authors’ institution Empa, which has
a separate listening and control room allowing for audio-visual supervision [19, 20]. AuraLab satisﬁes room
acoustical requirements for high-quality audio reproduction in terms of its background noise and reverberation
time. A 3D immersive sound system with 16 separate audio channels was used for the present study. Fifteen
loudspeakers “KH 120 A” (Georg Neumann GmbH, Berlin, Germany) were located in a hemispherical arrangement on three height levels (0, 30, and 60° elevation) in a distance of 2 m from the central listening spot.
Two subwoofers “KH 805” (Georg Neumann GmbH, Berlin, Germany) and a digital signal processor (DSP)
completed the playback system [19].
3.2 Stimuli
3.2.1 Recordings
The helicopter noise samples originated from ﬁeld recordings (landings and takeoffs) at Grenchen Airport,
Switzerland (ICAO code: LSZG) [19]. Details about the recordings can be found in [19]. The recordings
selected for this experiment were from helicopter types A109, A119, AS35, and EC20, all of which are categorized as light-weight civil helicopters (weight: 1.7-3.2 tons) [19, 21].
Background ambient sounds of birds and vegetation were recorded by means of an Ambisonic microphone
Soundﬁeld SPS200 (Soundﬁeld, London, UK) and an additional measurement microphone (to calibrate the system). The level (LAeq ) of the background ambient sounds was 37.5 dB(A) [19].
3.2.2 Processing
The recorded helicopter signals underwent a series of processing steps: preprocessing, optional propagation
ﬁltering, optional (minor) level shift, spatialization, and adding background ambient sound. Details about theprocessing steps are given by Taghipour et al. [19].
3.2.3 Stimuli set
Six ninety-second scenarios (i.e., stimuli) were generated, in which two (twice), three, four, or ﬁve (twice)helicopter events were present. Each individual helicopter event lasted 16 s (including fade-in and -out rampsof 2 s, arising from and disappearing in the ambient background sound). The ninety-second stimuli were also
faded in and out with 2-s ramps. Squared cosine ramps were used for all fadings. Figure 1 shows the leveltime histories (LAF curves) of the stimuli used for this experiment. The stimuli were played back at a sampling
frequency of 48 kHz and had an LAeq of 66.5 dB(A).
The number of ﬂight events, the absolute and relative durations of quiet periods, the IR, and the CMT are listed
in Table 1 for the individual stimuli. To calculate the absolute and the relative duration of quiet periods as wellas the CMT, periods without ﬂight events (of 16 s each) were deﬁned as “quiet.” That is, the backgroundambient sound of 37.5 dB was considered as a “quiet” (i.e., recreational) background.
Table 1. Measures of quiet periods for the stimuli.
Scenario
Number of events
Absolute duration of quiet periods, Tquiet (s)Relative duration of quiet periods (%)
Intermittency ratio, IR (%)
Centre of Mass Time, CMT (s)

S1
2
58
64.4
85.01
20.59

S2
2
58
64.4
84.04
19.48

S3
3
42
46.7
70.45
11.19

S4
4
26
28.9
52.83
6.15

S5
5
10
11.1
33.26
1.80

S6
5
10
11.1
26.16
1.80

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[dB(A)]AF

L[dB(A)]AF

L[dB(A)]AF

L[dB(A)]AF

L[dB(A)]AF

L[dB(A)]
AF

L

80
70
60
50
40
30

S1

80
70
60
50
40
30

S2

80
70
60
50
40
30

S3

80
70
60
50
40
30

S4

80
70
60
50
40
30
80
70
60
50
40
30

S5

S6

0

10

20

30

40

50

60

70

80

90

t (s)

Figure 1. Stimuli’s LAF curves. S1 to S2 stand for scenarios 1 to 6, respectively (see Table 1).

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Figure 2. Percentage relative frequencies for “more annoying” stimulus. In a stimuli pair, S and L represented
the stimuli with the smaller or the larger number of ﬂight events, respectively.
3.3 Experimental design
A two-alternative forced choice (2AFC) was given to the subjects, asking them whether they found the ﬁrst or
the second sound situation (i.e., stimuli) more annoying (in German: Finden Sie die erste oder zweite Geräuschsituation lästiger?). As a graphical help for the subjects, numbers “1” or “2” were displayed on the screen during
playback of the ﬁrst or the second scenario, respectively.
Three pairs of stimuli were prepared for playback. In each pair, one stimulus (L) had a larger number of ﬂybys
than the other (S); i.e., “4 vs. 2” (S4 vs. S1), “5 vs. 2” (S6 vs. S2), or “5 vs. 3” (S5 vs. S3). The order ofthe pairs appearance was randomized for each subject and balanced between subjects. Additionally, the orderof scenarios in each pair was randomized for each subject. In order to minimize the primacy and the recency
effects, each stimuli pair was played back twice before it was rated. With a short break in between repetitionsof stimuli pairs, each repeated pair playback took 6.5 minutes.
Furthermore, the ﬂight direction of the virtual acoustical scenarios was balanced between the subjects; i.e. either
takeoffs from right to left and landings from left to right, or vice versa.
3.4 Experimental sessions
The experiment was carried out as an individual focused listening test for each subject. After reading thestudy information and signing a consent form, the subjects answered the ﬁrst part of a questionnaire about their
hearing and well-being. Subjects made their 2AFC for the three pairs directly after the (repeated) playback ofeach pair. The listening test took 20 minutes. After the listening test, the subjects ﬁlled out the remaining partof the questionnaire about their demographic data.
3.5 Subjects
Fifty-six subjects (22 females and 34 males) participated in the experiment. They declared to have normal
hearing and to feel well. They were aged between 18 and 71 yr (median 43 yr).

4

RESULTS

In total, 168 comparisons were collected (i.e., three 2AFCs by 56 subjects). In 120 comparisons (71.4% of
the ratings) subjects found the stimulus with the larger number of ﬂight events (i.e., with less quiet time or,
equivalently, with a lower CMT or lower IR) more annoying than the other stimulus (Figure 2).
Figure 3 shows the ratings separately for the three stimuli pairs. For all three pairs, the stimulus with the larger

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Figure 3. Percentage relative frequencies for “more annoying” stimulus for each pair. S and L represented thestimuli with the smaller or the larger number of ﬂight events, respectively.
number of ﬂight events (L) and a smaller relative duration of quiet periods was chosen to be more annoying.
A chi-squared test showed no signiﬁcant differences between the pairs in predicting the relative frequency of“more annoying” [χ 2 (2) = 3.33, p = 0.19]. That is, whereas the results revealed that scenarios with larger
number of ﬂights (and less quiet time) were rated, on average, as being more annoying than their counterparts,
no signiﬁcant differences were found between different pairs (i.e., “4 vs. 2,” “5 vs. 2,” and “5 vs. 3”).
To further investigate possible effects of the indicators on annoyance, “relative” measures were coded between
the scenarios in the pairs. Percentage relative frequencies for “L more annoying than S” and a series of relative
measures between the two scenarios are listed in Table 2 for each pair. The dependence of the binary variable“L more annoying than S” on the relative indicators was analyzed by binary logistic regressions [22] which
estimated the probability of “L more annoying than S”. Generalized estimating equations [23] were used (SPSS
procedure GENLIN) to account for the repeated ratings of the subjects, as they predict a population-averaged
response [24, 25]. None of the relative indicators (listed in Table 2) were signiﬁcantly associated with the probability of “L more annoying than S.” This is in line with the above results of the chi-squared test. Nevertheless,relative quiet time, i.e., Tquiet,L /Tquiet,S , (p = 0.053) and relative CMT, i.e., CMTL /CMTS , (p = 0.061) and toa lesser degree also relative IR, i.e. IRL /IRS , (p = 0.094) tended to be linked with the probability of “L moreannoying than S,” while the relative number of ﬂights, i.e. NL /NS , was not linked (p > 0.999).
Table 2. Relative measures between the scenarios in the pair.
Pair (L vs. S)
Relative quiet time (Tquiet,L /Tquiet,S )Relative number of ﬂights (NL /NS )Relative IR (IRL /IRS )Relative CMT (CMTL /CMTS )Percentage relative frequencies for “L more annoying than S”

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4 vs. 2
0.45
2.00
0.62
0.30
62.5

5 vs. 2
0.17
2.50
0.31
0.09
75.0

5 vs. 3
0.24
1.67
0.47
0.16
76.8

5

CONCLUSIONS AND OUTLOOK

A psychoacoustic experiment was carried out to investigate the effect of quiet periods in helicopter noise scenarios on the perceived noise annoyance. All scenarios had an equal LAeq and differed only in the duration of quiet
periods and the ﬂyby events’ sound levels. The experiment revealed that, in direct comparisons, subjects rated
the scenarios with larger numbers of ﬂight events (and thus lesser amount of quiet periods) as more annoying
than scenarios with less events. Here, indicators based on the duration of quiet periods such as the Centre ofMass Time (CMT) or the relative duration of quiet periods tended to be particularly effective in predicting theprobability of “large number of events being more annoying than small number of events.” The results of thisstudy indicate that enabling longer quiet periods should be associated with a lower perceived annoyance. Thisoutcome is in accord with suggestions in the literature [6, 7, 8, 9, 10, 11, 12].
The chosen paired design of this study with two-alternative forced choices does not allow to further link theindicators directly to the perceived annoyance. In a different design (i.e. other than 2AFC chosen here), effectsof the measures for the duration of quiet periods (including CMT) on annoyance could be tested directly,
e.g., by direct scaling methods based on an annoyance rating using the ICBEN 11-point numerical scale [1].
Furthermore, similar studies could be conducted with road trafﬁc and railway noise stimuli to test whether thesenoise sources have similar effects. Finally, in future studies non-focused experiments should be performed totest the effects of quiet periods during activities such as reading.

ACKNOWLEDGEMENTS
The study presented here was partly funded by the Swiss Federal Ofﬁce for the Environment (Assignment
no. 5211.01228.100.01 and 5211.01520.100.01). The authors would like to express their deep gratitude to thesubjects of the experiment.

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