PROCEEDINGS of the
23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Sound Quality Evaluation of Refrigerated Truck Noise
Weonchan SUNG1; Patricia DAVIES2; J. Stuart BOLTON3
1,2,3

Ray W. Herrick Laboratories, School of Mechanical Engineering, Purdue University, U.S.A.

ABSTRACT
Noise from refrigeration units on trucks can be a problem, particularly when the trucks are parked nearresidences. The development of a sound evaluation method that takes into account the strength of all soundattributes affecting people’s responses, including level, is described here. Such a model, coupled with a soundprediction methodology, would be helpful to unit designers who are concerned with optimizing a unit’s
acoustical performance. The analysis of human subject responses to a variety of refrigerated truck sounds (inthe third of a series of three subjective tests) is described. Sixty participants rated 25 recorded and digitally
modified refrigerated truck sounds as well as 25 residential HVAC equipment sounds. Fourteen sound quality
metrics were evaluated as potential variables in a model to predict the average annoyance ratings of therefrigerated truck sounds. A rate-change-of-level metric that captured the impulsive character of therefrigerated truck unit noise was developed, and the best performing model comprised it, a level metric anda spectral balance metric. The performance of the model when used with signals and ratings taken from othertests is described.
Keywords: Sound Quality, HVAC

1. INTRODUCTION
In many countries, refrigerated trucks move goods that are susceptible to spoilage if they are not undera certain temperature. Refrigerated trucks can generate noise that affects people in nearby residences,whether the vehicle is moving or stationary with the refrigeration unit running (1). In addition,
reducing noise from a truck reduces driver fatigue, helps drivers to identify dangerous situation s, andcan reduce pedestrian and bike-related accidents (2). Here the investigation is on the development of
criteria to guide noise control for refrigerated truck units, and to improve their sound quality.
The main sound generating components of refrigerated truck units are fans, compressors, and adiesel engine. In Figure 1 is shown a typical power spectral density generated from a measurement
close to a truck refrigeration unit. It can be seen that the sounds consist of harmonic families from theengine (f0= 45 Hz), compressor (f0= 30 Hz), as well as a broadband component. The harmoniccomponents strongly affect the sound characteristics and therefore the sound quality of the refrigerated
truck unit. Sounds with high amplitude tones or high levels of broadband components may both be
loud, but the former increases the tonal character of the sound and the latter decreases it. The
distribution of sound energy across the spectrum, i.e., spectral balance, affects the quality of the sound.
Two closely-spaced tones can produce a beating phenomenon where the modulation frequency iscontrolled by the separation of the two tones. When this separation is around 1-8 Hz, variations inlevel are noticeable, and when it is around 40-80 Hz the level fluctuations are not noticeable but theygive the sound a rough character. If a short transient event is repeated continuously at regular intervals,
it may be perceived as impulsive or pounding. In many studies researchers have noted that various
sound characteristics can make sounds more annoying or more pleasant than would be predicted byconsidering level alone (3-8).
Although many sound quality studies have been conducted and various models or metrics proposedthat take into account various sound attribute strengths, A-weighted sound pressure level is still widely18024

sung26@purdue.edu

used as the only measure to quantify the sound. Sometimes, this is because the software to estimate
the sound attributes’ strengths is not available, and sometimes it is because the proposed metrics do
not work well. The latter problem may be due to sounds from only a small number of types of units
being used for the sound quality model development, thus limiting the applicability of the developedmodels. Sound attributes that may be important in general for a set of machine types, may not havebeen varied sufficiently in the sounds used in the model development, or may have co -varied withother sound attributes. In these cases, a proposed sound quality model may not apply to units with awider range of sound characteristics where attributes no longer co-vary and there is a greater varietyof levels of important sound attributes. A few models for diesel engine sound quality have beendeveloped and are used (7-11), however, they are usually tailored to only one application and cannot
be applied to another.
Reported in this paper is a study that builds on some previous research (12-14). The experimentdescribed is one of three experiments conducted to develop annoyance models of HVAC&R equipment
sounds. Various experimental methods and sounds were used to study people’s reaction to a large setof HVAC&R equipment sounds. The results presented in this paper are limited to refrigerated truck
unit sounds.

Figure 1 – Example power spectral density for a refrigerated truck unit. The red and blue dotted lines
with circles indicate the engine and compressor harmonics, respectively.

2. THE TEST DESCRIPTIONSIn previous tests (12-14), sounds with a wide range of loudness levels (10-50 sones) were presented
to subjects. This may have affected the way that subjects weighted the importance of sound attributes
other than loudness. In practice, other sound characteristics may be more important because theequipment under examination will compete with equipment types of similar size, and so the loudness
variations across units and operating conditions will not be so large within in this set of competing
equipment. In this test there were three parts: Parts A, B and C. Part A included quieter sounds (1535 sones), Part B included louder sounds (25-45 sones), and Part C consisted of a wider range of
loudness sounds (10-50 sones) as was used in previous tests. Half of the subjects, Group 1, evaluatedPart A sounds first then Part B and Part C, and the other half, Group 2, evaluated Part B sounds firstthen Part A and Part C. Purdue Institutional Review Board approval was received before testing began(IRB number 1507016324).
2.1 Procedures
When subjects arrived at the designated area, they were given an oral overview of the test. Then, theyread and signed the Consent Form, if they wanted to continue. Before starting the actual test, subjects’hearing threshold levels were checked. If their hearing thresholds in the octave bands between 125 Hz
to 8000 Hz were 20 dB or below, they could continue with the test. If their thresholds were higher
than 20 dB, they were paid $5 and did not continue to take the test.
Subjects read the following scenario before the test: ‘While you are listening, it may be helpful toimagine yourself in your garden, at any time during the day or evening, hearing these sounds
continuously.” Before starting each part of the test, subjects listened to 10 sounds which were chosen
to cover the range of the sound characteristics in that part of the test. Also, in each part, subjects
practiced rating two sounds, then rated the 50 sounds presented. There were 3-minute breaks between
each part. The order of sounds was randomly chosen for each part and for each subject. After all parts
were completed, the subject’s hearing thresholds were measured once again, and the subjects were8025

asked for comments on the experiment. Finally, the subjects were paid $10.
2.2 Test Environment and Playback
The test was conducted one subject at a time. The test sounds were played using a LynxONE soundcard, a Furman SP-20AB stereo amplifier, and Etymotic ER-2 research earphones. The subject hadaccess to a screen, keyboard and mouse, but the computer was outside the double walled IAC soundbooth where the subject took the test. The researcher used software to run each part of the test, which
controlled the playback of the sounds and automatically recorded the responses. T he subjects
evaluated the sounds by making a mark on a scale by using the mouse, after the sound was played.
The scale had five marks labeled “not at all annoying”, “slightly annoying”, “moderately annoying”
and “very annoying”. In the analysis, these marks were assigned values of 2.0, 3.5, 5 , 6.5 and 8.0,
respectively. Values from 1 to 9, with resolution controlled by the resolution of the screen, werepossible outcomes because subjects could mark anywhere on the line which extended beyond the firstand last marks.
2.3 Test Sounds
The tests included the sounds of both refrigerated truck units and residential units. In the test, bothrecordings and modified recordings were used. The sounds were modified to include many different
sound attribute levels and to reduce correlation between metrics across the set of sounds presented.
Thus, the correlations between sound quality metrics related to level, tonalness, spectral balance, andfluctuation were significantly lower than when only the original recordings were included. A total of
120 sounds were used in the test, with each part containing 50 sounds, with 15 of the sounds included
in all three parts of the test. These common sounds were included to allow adjustment of the annoyance
ratings if the subjects used the rating scales slightly differently from part to part. Table 1 contains
information on the sounds used in the tests. Part A was the quieter test, mostly consisting of residential
sounds, and Part B was the louder test, mostly consisting of refrigerated truck sounds.
Table 1 – Summary of the test signals
PartPart A (Quieter, mostly residential sounds)

Part B (Louder, mostly refrigerated truck sounds)

Pact C (Wider Loudness range)

Signals
50 Sounds
28 original, 22 modified
36 residential, 14 refrigerated truck
50 Sounds
30 original, 20 modified
11 residential, 39 refrigerated truck
50 Sounds
19 original, 31 modified
24 residential, 26 refrigerated truck

2.4 Subjects
The subjects were recruited through Purdue online bulletin boards and flyers posted on the Purdue
campus and in local community buildings. Of the 62 subjects who volunteered to take the test, 60
passed the hearing test. Thirty of them identified themselves as male and 30 as female. The subjects
ages were from 18 to 62 years, the average age was 28.4 years and the median was 26 years. Thirtytwo test participants grew up in the U.S., 25 subjects grew up in Asia (15 East Asia, 10 India), 1
subject grew up in South America, and 2 subjects grew up in Africa. Most of the subjects has noknowledge of sound quality and noise control. Five of the participants have previously participated insimilar experiments, and 3 sometimes participate in musical events.

3. RESULTS AND DISCUSSION
The average of the subjects’ annoyance ratings and the standard deviation of estimated mean
(standard error) were calculated for each of the two subject groups and for each part of the experiment.
Test results, correlation between average annoyance ratings and sound metrics, sound metric8026

adjustments, and performance of various linear annoyance models are described in this section.
3.1 Average Annoyance Ratings
The average annoyance ratings for the signals are shown in Figure 2. Results for Group 1 and Group2 subjects are shown separately. Group 1 subjects (red) heard the quieter sounds (Part A) first, while
the Group 2 subjects (blue) heard the louder sounds (Part B) first. Part A sound ratings from Group 2
subjects were slightly lower than those from Group 1 subjects, and Part B sound ratings from Group1 subjects were slightly higher than those from Group 2 subjects. This indicates that the subjects’
responses were affected by the order of the parts in the experiment. Trends in signal ratings are similar.
Part A and Part B ratings were adjusted so the ratings of the common sounds in Parts A, B and C
matched (on average). The adjustments were made by first fitting a straight line to the Part C versus
Part A (or Part B) responses for the common sounds, and then using the estimated intercepts andgradients of the lines to adjust the averaging ratings of all the signals in Part A and Part B. These
adjusted Part A and Part B ratings were used along with Part C ratings in the model development.

Figure 2 – Average annoyance rating with standard error bars. (a) Part A ratings – quieter test, (b) Part
B ratings – louder test, and (c) Part C ratings – wider loudness range test. Red is for Group 1: PartAÆBÆC, blue is for group 2: Part BÆAÆC.
3.2 Metrics and Adjustment of Metrics
The sound metrics examined are listed in Table 2. Most of the metrics were calculated by using Head
Acoustics’ ArtemiS software and some were programed and calculated using MATLAB. The
thresholds for sound metrics were calculated assuming that while the different values of the metric at
the lower end of the metric range do reflect differences in the sound characteristics, these differences
did not greatly affect the annoyance ratings for those sounds. Adjusted Tonality and adjusted
Sharpness were calculated by thresholding Tonality and Aures’ Sharpness. When calculating thethresholds for Tonality and Aures’s Sharpness, the average annoyance results of both residential unit s
and refrigerated truck units were used even though the annoyance prediction models were developed
separately. The R 2 values for four-metric linear models (metrics: N 5, S A5adj, T 5adj , R5 ) with different
adjustment thresholds were calculated and the thresholds that resulted in the highest R 2 values wereselected. The Sharpness and Tonality thresholds for this model were thus set at 2.5 acum and 0.27 tu,

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respectively. If the value of Tonality or Aures’ Sharpness is less than the threshold, the adjusted metricis zero, and if it is larger, it is set to the original metric value – the threshold.
In the second test (in the series of tests leading to the experiment described here), which was asemantic differential test, the fourth attribute in the factor analysis of the responses to refrigerated
truck sounds was aligned with the ratings on the “Impulsiveness” scale (14). Also, in the first of thisseries of tests in which subjects wrote down words or sentences to describe the sounds, many subjects
used words and descriptions that are related to impulsiveness, when describing sounds with diesel
engine components (13). So, in addition to the commonly considered sound metrics for annoyancemodels, several impulsiveness-related metrics were also considered. The most effective impulsivenessmetric (when included in the annoyance models) was the maximum rate of change of the loudness(RCL), a metric that has been used in assessment of transient environmental sounds (15). To calculate
RCL, Zwicker loudness time histories were calculated by using the Head ArtemiS software and thena 15-point digital FIR filter (designed using the firpm program in MATLAB) was used to calculate thederivative of the loudness time history. The maximum rate of change of loudness was used to quantify
the impulsiveness characteristic.
Table 2 – Sound metrics examined in the test. The “adj” indicates that the metric value is adjusted.
Threshold for
Metric Symbol
Metric Description
Adjusted Metric
N5
Zwicker time varying Loudness exceeded 5% of the time
N/A
SVB5
Von Bismarck Sharpness exceeded 5% of the time
N/A
SA5 Æ SA5adj
Aures Sharpness exceeded 5% of the time
2.5 acumT5 Æ T5adj
Tonality metric exceeded 5% of the time
0.27 tuK, RCL
Impulsiveness – Kurtosis and Loudness derivative
N/A
R5
Roughness (16) exceeded 5% of the time
N/A
FS5
Fluctuation Strength (16) exceeded 5% of the time
N/AdBA, dBC
A or C weighted sound pressure level
N/A
SQI*
SQI (6) calculated from sound pressure not sound power
N/APA
Psychoacoustic Annoyance (16)N/A
3.3 Annoyance Models
The annoyance prediction models for the refrigerated truck sounds, are a linear combination of someof the sound metrics shown in Table 2. All combinations of metrics were considered and the models
with the highest R 2 values were selected, but models where each metric represented streng ths ofdifferent sound attributes were preferred, because those attributes could be directly related to signal
characteristics, and thus to components in the machine. The average of the annoyance ratings plottedagainst annoyance predictions for the best 1, 2 and 3 metric models are shown in Figure 3. Hereresponses from all three parts of the test were used to estimate the model coefficients. The mostaccurate single-metric predictor is N 5, see Figure 3(a). Inclusion of the adjusted Aures’ Sharpness
metric in the model increases the R 2 value by 0.02 (Figure 3(b)). Unlike the residential annoyanceprediction model, see (17) for details, including the adjusted Tonality and Roughness metrics in themodel did not improve annoyance predictions for the refrigerated truck sounds. However, by including
the RCL metric as the third metric in the model, the R2 value increased by 0.01 (Figure 3(c)). The
three models estimated using only the 26 responses to refrigerated truck sounds in Part C of the test
were used to predict the responses to refrigerated trucks sounds in Part A (14 sounds) and Part B (39sounds) of the test. The results are shown in Figure 4. Responses to Part A and Part B sounds areaccurately predicted by the Part C model. Inclusion of S A5adj and RCL in the model helped to improve
the prediction accuracy. From Figure 4 it can be seen that when the three-metric model was used, Part
B sounds were noticeably closer to the one-to-one line (black) than when using the two-metric model.

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Fig. 3 – Average of annoyance ratings of refrigerated truck unit sounds plotted against annoyancepredictions. The standard deviations of the estimated average annoyance range between 0 .12 And 0.23.
The markers indicate Part A (●), Part B (x), and Part C (∆) sounds.

Fig. 4 – Average of annoyance ratings in Part A and Part B plotted against annoyance predictions frommodels developed using responses in Part C only. The markers indicate Part A (●) and Part B (x)sounds

4. CONCLUSIONS AND DISCUSSIONSThe third in a series of tests focused on evaluation of HVAC&R equipment noise has been described
here. Subjects rated sounds in the three parts of this test. Part A contained ratings of quieter sounds,
Part B louder sounds, and Part C sounds that spanned a wide loudness range. There were only slightdifferences in the results from the group that did Part A first and the Group that did Part B first. In
both cases there tended to be slight overshoot. The group that heard the quieter sounds first rated thelouder sounds higher than the group that heard the louder sounds first. Similarly the group that heard
the louder sounds first rated the quieter sounds lower than the group that heard the quieter sounds
first. The performance of one-, two- and three-metric models when predicting the annoyance ratingsof refrigerated truck unit sounds was examined. It was found that results could be improved byadjusting two of the metrics so that they only played a role in the model above a certain thresholdvalue. The most important metric for an annoyance prediction was Zwicker Loudness exceeded 5%of the time (N5 ). The second most important metric was adjusted Aures’ Sharpness, and inclusion of
the maximum rate of change of the loudness (RCL) in the model was also significant, particularly forpredicting the annoyance of the louder sounds. The models estimated using responses to Part C sounds
only, also predicted the responses to sounds in Part A and Part B very accurately.

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ACKNOWLEDGEMENTS
Thanks to D.J. Carr at the Ray W. Herrick Laboratories for help in the design and preparation of thistest. Also thank to industry sponsors for their support, insights and advice.

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