PROCEEDINGS  of  the     23rd  International  Congress  on  Acoustics       9  to  13  September  2019  in  Aachen,  Germany  Acoustic  comfort  assessment  in  heavyweight  residential  buildings:  acoustic  data  associated  to  subjective  responses.  
Nikolaos Georgios VARDAXIS1; Delphine BARD2
1
2

Lund University, LTH, SwedenSaint-Gobain Nordic & Baltic Delegation, Denmark / Lund University, LTH, Sweden

ABSTRACT  This article presents a study aiming to explore and evaluate acoustic comfort in residential multistory
buildings in Sweden. Acoustic data was associated to self-reported responses acquired by a survey: aquestionnaire was setup researching the response to noise annoyance from multiple sources in a flat and theemotional reactions of tenants to the acoustic climate at home. An assessment of acoustic comfort in the test
apartments was performed utilizing the circumplex model of affect. A sample of 353 residents offered theirratings on 12 bipolar scales regarding their feelings towards their living sound environment. Two dimensions
were identified: pleasantness and activation. Statistical models were developed using acoustic and structuralvariables. 𝐿′#$,&,'(( predicted best pleasantness and number of flats per building predicted best activation. A
new acoustic comfort indicator is suggested based on the pleasantness model and four novel acoustic comfort
classes are proposed as: AC-1: Very good, AC-2: Good, AC-3: Acceptable, AC-4: No comfort.
Keywords: Acoustic, Comfort, Responses

1.   INTRODUCTION  The Cambridge dictionary defines comfort as “a pleasant and satisfying feeling of being physicallyor mentally free from pain and suffering, or something that provides that feeling” (1). Seemingly,
comfort is described as a state of feelings towards a situation. Acoustic comfort is defined in (2) as: “aconcept that can be characterized by absence of unwanted sound, desired sounds with the right leveland quality, and opportunities for acoustic activities without annoying other people”.
Acoustic comfort issues have been treated entirely as noise annoyance problems so far. Usually
acoustic data (sound insulation descriptors) were associated to self-reported noise annoyance of theresidents (3-6). A detailed review of field surveys following that approach is provided in (7).
Another approach for the evaluation of acoustic environments has been taken with soundscapes.
Soundscape is: “an acoustic environment as perceived or experienced and/or understood by a person or
people, in context” (8). Background ambience and several random sounds can comprise a soundscape(9). It can be an outside public space: a street or park. The same for indoor climates, such as the living
sound environment of an apartment. Assessment of soundscapes can utilize empirical data (interviews)
or surveys (questionnaires), as in this study. Subjects can offer ratings on certain scales about asoundscape. Then statistical analysis can reveal the underlying dimensions describing how subjects
perceive it. In (10) principal components analysis (PCA) was performed for soundscape perception
from ratings on 116 attribute scales of 50 recorded outdoor urban soundscapes. The dimensions ofpleasantness, eventfulness and familiarity explained most of the total variance. In (11) visual andacoustic experiments were conducted for the perceived similarity of soundscapes, using 50 recordings
from (10). Multidimensional scaling (MDS) revealed three dimensions: distinguishable-indistinguishable sound sources, background-foreground sounds and intrusive-smooth sound sources.
In (12) a prediction model was developed for the dimension of vibrancy in soundscapes based on
acoustic and visual parameters. There is experimentation with the soundscapes approach in overall, forthe evaluation of outdoor spaces, but less applications of soundscapes for indoor spaces.
In this study, we approach acoustic comfort in apartment buildings utilizing soundscapes and
1
2

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nikolas.vardaxis@constuction.lth.sedelphine.bard@ constuction.lth.sefocusing on human perception and emotions. We explore how the residents feel in their living soundenvironment. A model of underlying dimensions was employed, namely the circumplex model ofaffect, a tool developed in psychology to study emotional reactions of subjects (13). The affectcircumplex has been applied in assessment of core affects (13,14) and soundscape studies (10-12).

2.   METHODS  The study sample includes 31 structures of various types: heavyweight or lightweight. The term
heavyweight (HW) refers to typical concrete frame structures and the term lightweight (LW) refers towooden buildings (cross laminated timber frame). In total there are 94 building units from 31 blocks (1or more units each) of a certain structure: 24 HW types and 7 LW. Sound transmission measurements
took place in the test structures between two typical adjacent rooms, one above another, bedrooms orliving rooms. Current standardized methods for airborne sound reduction and impact sound levelmeasurements were followed to characterize insulation of building components according to ISO(15,16). The measurement data were collected from the Green Buildings database, which concerns a
Swedish national program about acoustic conditions in dwellings. An overview of the acoustic
variables is provided in Table 1. Most structures fulfil the Swedish BBR criteria, which set minimum
𝐷′#$,&,*( =52dB from outside to inside a house and maximum 𝐿′#$,&,*( =56dB (17).
Table 1 – Single number quantities of acoustic descriptors for the sample structures.
Airborne sound descriptors
Impact sound descriptors
𝑫'𝒏𝑻,𝒘,𝟓𝟎
𝑫'𝒏𝑻,𝒘,𝟏𝟎𝟎
𝑳'𝒏𝑻,𝒘,𝟓𝟎
𝑳'𝒏𝑻,𝒘,𝟏𝟎𝟎
Structure typeN

Heavy-weight (HW)
Light-weight (LW)
All structures

24
7
31

Mean (Range)
58.3dB (51-64)
55.5dB (48-63)
57.6dB (48-64)

58.7dB (52-65)
56.3dB (48-65)
58.1dB (48-65)

Mean (Range)
49.6dB (40-53)
52.4dB (49-59)
50.2dB (40-59)

49.1dB (39-52)
49.5dB (47-54)
49.2dB (39-54)

Furthermore, self-reported data was collected with the development of a questionnaire, for the
residents of the test structures, developed according to ISO 15666 (18). The survey aimed to capture
several aspects relevant to acoustic comfort. It was distributed using post mail (one copy for every test
flat, a web link was provided too): an invitation letter was firstly sent with the questionnaire, then two
reminder letters followed within a month. Table 2 presents the question items analyzed in this article.
Table 2 - Question with semantic differentials as presented a survey the about acoustic environment
at home. Original version presented in Swedish language as developed in (14).
Different environments can affect the way we feel and our well-being. What effect does yourhome have on you? Answer each one by circling the number that most accurately describes
the way you feel when you come home. Don’t spend too much time on each question – we arelooking for your immediate reaction. These are scales of opposites, so if you feel more drowsy thanalert, circle either number 1 or 2 on the scale. If you are right in between, circle number 3.
a. Sleepy
1
2
3
4
5
Awake
b. Displeased
1
2
3
4
5
Pleasedc. Bored
12345Interestedd. Tense
12
3
4
5
Serenee. Passive
1
2
3
4
5
Activef. Sad
1
2
3
4
5
Glad
g. Indifferent
1
2
3
4
5
Engagedh. Anxious
1
2
3
4
5
Calm
i. Dull
1
2
3
4
5
Peppy
j. Depressed
1
2
3
4
5
Happy
k. Pessimistic
1
2
3
4
5
Optimisticl. Nervous
1
2
3
4
5
RelaxedWith a response rate of 27%, 353 observations were collected (158 male, 188 female, 7 unreported).
The subjects are 18-85 years old and have spent at least 12 months in their flat. Those who use hearing
aids at home were filtered out of the dataset. Tenants living on the top floor were filtered out too, since
they do not have neighbors on the floor above and their sound conditions are probably different.

837

The question items regarding the emotional reactions and perception evaluation of the participants
are presented in Table 2. It is simply formulated as: What effect does your home have on you? Thequestionnaire was entitled “Research project on sound environment in residential buildings”. The
introduction text as well as most of the questions concerned acoustic issues at home. The results wereanalyzed in SPSS Statistics 24. PCA was performed for dimension reduction. Linear regression wasapplied the component loadings in order to develop prediction models for the identified dimensions.
Non-parametric Mann-Whitney U-tests were applied to compare independent groups of observations.
Figure 1 depicts the circumplex model of affect as defined in (13,14). It refers to a psychologicalconstruct composed of two orthogonal dimensions: pleasantness and activation. They were foundsufficient to express the emotional state of subjects and the 12 items of Table 2 were established andvalidated after experiments for Swedish wording in a study by Västfjäll et al. (14).

Figure 1 - The affect circumplex model presented in (14).

3.   RESULTS  AND  DISCUSSION  3.1   Individual  observations  analysis  The mean responses of the residents for the question under study are illustrated in Figure 2. From
the total 353 observations, 327 were included in this analysis due to missing values. As can beobserved all self-reported rating averages of the participants are on the positive side of the scale (>3),
meaning on the side of the reaction scales with the affirmative emotions.

Figure 2 – Mean responses for the sub-items of
the question: What effect does your home have on
you? Error bars represent 95% C.I.

Figure 3 – Component loadings plot for the twodimensions: 1-pleasantness and 2-activation.

Principal components analysis was performed and the Kaiser-Meyer-Olkin (KMO) measure ofsampling adequacy offered a sufficient value of 0.948. Varimax rotation was applied to achieve an
optimal orthogonal solution. Twelve components were extracted in total but only two of them wereselected, after applying a scree criterion based on a minimum eigenvalue of unity. The percentage ofthe total variance explained was 39.4% and 36.2% for the first and second components respectively.
That is a satisfactory solution explaining a cumulative 75.6% of the total variance. The component
loadings are presented in Table 3 and their plot on two dimensions is illustrated in Figure 3. The
components can be directly interpreted as the two dimensions of pleasantness and activation, as
suggested by the valence-activation construct analyzed in (14).

838The first component corresponds to the dimension of pleasantness since the adjective pairs:
displeased-pleased, sad-glad, depressed-happy load higher on that and they are designed to measurepleasantness emotions (Table 3). The adjective pairs that load higher on the second component are:sleepy-awake, dull-peppy, passive-active, which supposedly measure the dimension of activation.
Table 3 – Component loadings of final the PCA rotated solution.
Semantic differentials
Component 1
Component 2(Pleasantness)a. Sleepy – Awake
b. Displeased – Pleasedc. Bored – interestedd. Tense - Serene
e. Passive – Active
f. Sad – Gladg. Indifferent – Engaged
h. Anxious – Calmi. Dull – Peppy
j. Depressed – Happyk. Pessimistic – Optimisticl. Nervous - Relaxed% of variance explained

0.657
0.538
0.816
0.700
0.469
0.872
0.640
0.680
0.843
39.38%(Activation)
0.782
0.458
0.644
0.807
0.521
0.746
0.833
0.607
0.558
36.16%

Coefficients below 0.40 are suppressed.

All the components load on the positive region for both dimensions (Figures 1 and 3). That is
specifically the area of “pleasant activation” as explained in (14). Consequently, the residents perceive
their sound environment at home as having a high degree of acoustic comfort in overall. This is
expected since most sample buildings comply with the Swedish criteria (17).
Further, we explored possible predictor variables for modeling the identified PCA dimensions. The
component loading scores of every observation were used as dependent variables. Other variablesfrom the survey’s dataset can be used as independent variables to establish statistical associations withthe use of linear regression models. The aim is to develop a prediction model for acoustic comfort
using acoustic descriptors and building construction data. The determination coefficients R 2 for linear
models predicting the components’ loading scores are shown in Table 4. The R 2 represent the total
variance explained by the predictor variable. All R 2 values are very low, indicating lack of strong
linear relationships probably due to the high variability between all 327 observations. Consequently,
no further conclusions could be drawn using the individual responses.
Table 4 - Determination coefficients R 2 for linear predictors of Pleasantness and Activation(individual observations case).
Predictors

Component 1

Component 2

𝐿′#$,&,'((
𝐷′#$,&,'((
𝐿′#$,&,*(
𝐷′#$,&,*(
Size (m2)#Flats#Tenants

Pleasantness
0.005
0.001
0.003
0.002
0.001
0.014
0.012

Activation
0.033*
0.007
0.006
0.011
0.077*
0.039*
0.002* (Model parameters significant with p<0.05)
3.2   Clustered  observations  analysis  for  heavyweight  buildings  The observations are clustered in structure types such as: heavyweight (HW) concrete structuresand lightweight (LW) wooden ones. It has been indicated previously that HW and LW structures havequite different acoustic behavior and the perception of residents varies according to structure type839

(3,5,21). In this survey, the mean responses for of LW structure groups are higher than HW ones andfor 5 items there are statistically significant differences. That was suggested by non-parametricMann-Whitney U-tests, which indicated significance specifically for items: a (Z=-3.769, p<0.001), c(Z=-2.738, p<0.01), e (Z=-2.132, p<0.05), g (Z=-2.016, p<0.05) and i (Z=-2.540, p<0.05).
Additionally, there are not equal sample sizes of observations in the various structure blocks. Thus
HW and LW structures are studied separately. Also, the responses are now averaged per structure
block: so the replies from a certain structure type are represented by their mean value. For the concrete
structures, the same analysis was attempted with better results than in Table 4. However, the R 2 values
went as high as 0.2, which is not a sufficient level of correlation. Thus all groups with small sample
size were filtered out completely and 9 HW buildings having a sample size n more than 10
observations were analyzed. Finally, 181 observations were included from 9 blocks of heavyweight
structures (85 male, 96 female). The initial PCA statistics provided: KMO=0.934, 37.3% and 34.6% of
total variance explained by 𝐷' :pleasantness and 𝐷4 :activation respectively. Using the 9 blocks (n>10)
led to better linear associations for the tested variables, as seen in Table 5.
Table 5 – R 2 for linear predictors of Pleasantness and Activation in concrete buildings(HW clustered observations).
Predictors
Size (m2)#Flats#Tenants
𝐿'#$,&,*(
𝐿'#$,&,'((
𝐷'#$,&,*(
𝐷'#$,&,'((
𝐷'#$,&,*( + 𝐿'#$,&,*( + Size + #Flats
𝐷'#$,&,'(( + 𝐿'#$,&,'(( + Size + #Flats

Component 1

Component 2

Pleasantness

Activation

0.270
0.117
0.108
0.345
0.478*
0.009
0.002
0.479
0.573

0.136
0.538 *
0.017
0.248
0.192
0.351
0.264
0.708
0.647

* (Model parameters significant with p<0.05)

The impact sound index 𝐿′#$,&,'(( is a statistically significant predictor of Pleasantness while thenumber of flats in a building is a significant predictor of Activation. As for the other acoustic
indicators, the airborne sound reduction indices 𝐷′#$,&,'(( and 𝐷′#$,&,*( associate well with thedimension of activation only, though with moderate R 2 values. However, number of apartments in abuilding unit (variable denoted #Flats) correlates high enough with activation.
Combinations of the predictor variables were tested in order to develop multiple regression models.
The best determination coefficient R 2 is achieved for the relevant variables: both descriptors 𝐿′#$,&,'(( ,𝐷′#$,&,'(( (or 𝐿′#$,&,*( , 𝐷′#$,&,*( ) the size of flat and the number of flats in a building. But thosemodels do not have statistical significance for their model parameters (Table 5). The same applies tomost variables regardless the R 2 indicated in the models, simple or multiple. Only the univariate
models of 𝐿′#$,&,'(( and #Flats predicting pleasantness and activation respectively have statisticallysignificant parameters (p<0.05). A backwards regression process was performed for a model with all
variables of Table 5. The results confirmed that the only significant predictors are 𝐿′#$,&,'(( for 𝐷'
and #Flats for 𝐷4 . Only those can formulate reliable prediction models of the PCA dimensions as:
𝐷' = 4.171 - 0.084·𝐿′#$,&,'((
𝐷4 = 0.321 - 0.009·#Flats
Furthermore, using size for a linear model with averaged responses per structure block would not
be that reasonable. Size of flats varies within a building and an average size might not be
representative of the conditions for all subjects. But the number of flats in a structure is constant (atleast in this dataset) and relevant to average responses. Also more flats and residents in a building
mean more activity and sounds between apartments, so #Flats is sensible to correlate with activation.
Reasonably the impact sound descriptor associates higher with pleasantness, which is related toquietness and noise annoyance. Impact sound descriptors 𝐿′#$,&,'(( or 𝐿′#$,&,*( have been found to behighly correlated to impact noise types in apartments. The latter have been reported as the most
disturbing noise type during numerous subjective annoyance surveys (3-7,19-21).

840

3.3   Proposal  of  acoustic  comfort  index  for  heavyweight  buildings  An acoustic comfort index can be constructed based on the aforementioned models for theprediction of pleasantness and activation. A parametric analysis was performed illustrating theacquired component loadings for various values of 𝐿′#$,&,'(( and #Flats in the models for 𝐷' and 𝐷4
respectively (Figures 4 and 5). The desirable values for component loadings lie in the region of“pleasant activation” (Fig.1), which corresponds to positive loadings for both dimensions. Loadings
bigger than 0 are necessary for positive emotional reactions and good acoustic comfort evaluation.
Values bigger than 0.5 would indicate a very good evaluation in the affect circumplex and a high senseof acoustic comfort.
Parameter analysis for model D1: Pleasantness

Parameter analysis for model D2: Activation1

1
Linear model
BBR thresholdsection lines

0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
-1
35

Linear model
section lines

0.8

Activation component loading

Pleasantness component loading

0.8

0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
-1

40

45

50

55

60

Impact sound descriptor L' nTw,100

Figure 4 - Parameter analysis in the modelof 𝐷' :Pleasantness predicted by 𝐿′#$,&,'(( .

65

0

5

10

15

20

25

30

35

40

45

50

Number of flats

Figure 5 – Parameter analysis in the modelof 𝐷4 :Activation predicted by #Flats.

For the case of the impact sound index as predictor of 𝐷' :Pleasantness, it is observed that the valueof zero corresponds to 50dB, so above that there is a region of good acoustic comfort with positive
ratings in the affect circumplex. Then above a threshold of 0.5 there is a region of very good sense of
acoustic comfort, which corresponds to 𝐿′#$,&,'(( values lower than 44dB. Below a component
loading of zero there is a suggested region for an acceptable comfort level between 51-56dB. The 56dB
value is due to the highest limits set in the Swedish regulations for noise transmission (17), also known
as BBR value of Boverket. However, the 56dB maximum impact noise level is established with thedescriptor 𝐿′#$,&,*( , including the low frequency range from 50Hz. Overall, Table 6 summarizes thoseregions, according to which an acoustic comfort index with distinct comfort classes is proposed.
For the number of flats predicting the dimension of activation (𝐷4 ), the parametric analysis does notoffer very clear results (Fig.5). The linear model intersects the zero line at 35, meaning that less than
35 apartments per building unit would be required for good acoustic comfort sense. Then the limit of
0.5 is out of the scope of comparison and no further conclusions can be made about the number of flats
and the activation loadings. Further, 35 is not a small number for total flats per building thus it isquestionable if that number can really be a parameter for an acoustic comfort index. Considering allthat, the acquired linear model for predicting 𝐷4 :Activation was neglected. To formulate a new
acoustic comfort descriptor, only the model of 𝐿′#$,&,'(( predicting pleasantness was utilized. The
equation for the new proposed acoustic comfort indicator is then:𝐴𝐶7#89: = 4.171 - 0.084·𝐿′#$,&,'(( .
Noticeably the new acoustic comfort index should take values between -1 and 1, an assumption
compliant with the maximum or minimum component loadings. The linear model can return values
outside the reasonable limits [-1,1] but such values are neglected. Positive values are needed for a goodevaluation. The condition 𝐴𝐶7#89: >0 can help identify the suggested acoustic comfort classes: AC-1
or AC-2, as tabulated in Table 6. The threshold value of 0.5 for an average component loading
separates AC-1 and AC-2 characterized as “Very Good and “Good” respectively.
The negative values correspond to the lower comfort classes AC-3 and AC-4, that being thecategories characterized as “Acceptable” and “Not acceptable” respectively (Table 6). The AC-3
region (𝐴𝐶7#89: values between -0.5 and 0) relates to low comfort evaluation but still acceptable841

according to the Swedish regulation limits (17). The index values below -0.5 denote the worst region
for acoustic comfort perception, that is the class AC-4.
Table 6 presents also a comparison with the acoustic classes established by the Swedish acoustic
standard (21) which uses 𝐿′#$,&,*( instead for impact sound level descriptor. Class D has the same
maximum limit as the suggested AC-4: impact sound level index more than 56dB correspond to theworst class. Then Class C is defined for 𝐿′#$,&,*( values between 56-53dB, Class B for values
52-48dB and Class A for 𝐿′#$,&,*( values lower than 48dB. The values of the suggested classes AC-3,
AC-2 and AC-1 are a bit lower, meaning that the acoustic comfort classes derived in this study havestricter criteria than the standardized classes.
Table 6 - Acoustic comfort index and classes suggestion for heavyweight structures. Comparison
with Swedish classification of SIS SS 25267 standard (21).
Comfort category
No comfort Acceptable
Good
Very goodIndex class

AC-4

AC-3

AC-2

AC-1

𝑨𝑪𝒊𝒏𝒅𝒆𝒙< -0.5-0.5 - 0

0.01-0.5> 0.5

𝑳′𝒏𝑻,𝒘,𝟏𝟎𝟎> 56dB*

56-51dB

50-45dB< 44dB

Swedish standard (23)Class DClass CClass BClass A

𝐿′#$,&,*(> 56dB*

56-53dB

52-48dB< 48dB* BBR minimum value (17)3.4   Clustered  observations  analysis  for  lightweight  structures  The same analysis was performed for the case of LW structures in order to find variables thatpredict the PCA dimensions and formulate a similar acoustic comfort model as before. Namely, 77
observations from 6 blocks of LW structures were included. A minimum sample size of n=5 per LW
block was applied, due to less groups and observations. Initial PCA provided: KMO=0.923, 45.2% and34.8% of total variance explained by 𝐷' :pleasantness and 𝐷4 :activation respectively. However, nolinear model had statistical significance in model parameters to be reliable enough. Hence, aconcluding acoustic comfort model for LW structures could not be proposed.

4.   CONCLUSIONS  The acoustic comfort was investigated in a sample of Swedish apartment buildings. A comfort
assessment was performed, based on the emotional reactions of the residents towards their soundenvironment at home. The circumplex model of affect (14) was deployed for evaluation. The results
indicated a very positive perception in overall according to the semantic differential scales used inmodel, indicating affirmative emotional states of the residents in their apartments.
Principal component analysis was performed and two dimensions were identified: pleasantness andactivation, which explain 39.4% and 36.2% of the variance respectively, namely 75.6% of the total
variance. This is a confirmation of the dimensions suggested by the affect circumplex model (13) andespecially for the Swedish version with 12 sub-items used in this study case (14).
The development of statistical models was attempted based on the prediction of component loadingscores by variables relevant to the structure and the acoustic conditions. The acoustic descriptors
𝐿′#$,&,'(( and 𝐷′#$,&,'(( , the size of apartments, the number of occupants in a flat and the number of
total flats in a building were tested.
Linear models could not be developed for the case of individual observations due to high variability
in the dataset. However, when the observations were treated as grouped in structures and their
responses were averaged per structure block, sufficient correlations could be established. For the caseof heavyweight (HW) concrete buildings, prediction models were developed for the two identified
dimensions. 𝐿′#$,&,'(( was the best predictor for 𝐷' :pleasantness and number of flats predicted best
the dimension 𝐷4 : activation. Multiple regression models were tested as well, but they failed in termsof statistical significance for their estimated model parameters.
Furthermore, a novel acoustic comfort index for concrete buildings is suggested, based on thestatistical model for the prediction of pleasantness. The suggested descriptor is formulated as:
𝐴𝐶7#89: = 4.171-0.084·𝐿′#$,&,'(( . Based on the new index and its scale, 4 classes of acoustic comfort

842

are suggested as AC-1: Very good, AC-2: Good, AC-3: Acceptable, AC-4: No acoustic comfort.
For the lightweight (LW) wooden building structures of this survey, the statistical results were notsufficient for prediction models. The total observations and the sample size of LW blocks were much
lower than for the HW data groups. Further individual research should be applied in lightweight
structures to establish a separate model for acoustic comfort.

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