A Study on the Sound Quality
Evaluation Model of Mechanical
Air-Cleaners
Jeong-Guon Ih1
e-mail: j.g.ih@kaist.ac.kr
Su-Won Jang
Cheol-Ho Jeong2
Department of Mechanical Engineering,
Center for Noise and Vibration Control 共NoViC兲,
KAIST,
Daejeon 305-701, Korea
Youn-Young Jeung
Tech. Center,
Woongjin Coway Co., Ltd.,
Seoul 151-818, Korea
In operating the air-cleaner for a long time, people in a quiet
enclosed space expect low sound at low operational levels for a
routine cleaning of air. However, in the condition of high operational levels of the cleaner, a powerful yet nonannoying sound is
desired, which is connected to a feeling of an immediate cleaning
of pollutants. In this context, it is important to evaluate and design
the air-cleaner noise to satisfy such contradictory expectations
from the customers. In this study, a model for evaluating the sound
quality of air-cleaners of mechanical type was developed based on
objective and subjective analyses. Sound signals from various aircleaners were recorded and they were edited by increasing or
decreasing the loudness at three wide specific-loudness bands:
20–400 Hz (0–3.8 barks), 400–1250 Hz (3.8–10 barks), and 1.25–
12.5 kHz bands (10–22.8 barks). Subjective tests using the edited
sounds were conducted by the semantic differential method (SDM)
and the method of successive intervals (MSI). SDM tests for seven
adjective pairs were conducted to find the relation between subjective feeling and frequency bands. Two major feelings, performance and annoyance, were factored out from the principal component analysis. We found that the performance feeling was
related to both low and high frequency bands, whereas the annoyance feeling was related to high frequency bands. MSI tests using
the seven scales were conducted to derive the sound quality index
to express the severity of each perceptive descriptor. Annoyance
and performance indices of air-cleaners were modeled from the
subjective responses of the juries and the measured sound quality
metrics: loudness, sharpness, roughness, and fluctuation strength.
The multiple regression method was employed to generate sound
quality evaluation models. Using the developed indices, sound
quality of the measured data was evaluated and compared with
the subjective data. The difference between predicted and tested
scores was less than 0.5 points. 关DOI: 10.1115/1.3085889兴
1
Corresponding author.
Present address: Department of Electrical Engineering, Technical University of
Denmark, DK-2800 Kgs. Lyngby, Denmark.
Contributed by the Technical Committee on Vibration and Sound of ASME for
publication in the JOURNAL OF VIBRATION AND ACOUSTICS. Manuscript received January
20, 2008; final manuscript received October 23, 2008; published online April 21,
2009. Assoc. Editor: Stephen A Hambric. Paper presented at the 2007 ASME International Mechanical Engineering Congress 共IMECE2007兲, Seattle, WA, November
10–16, 2007.
2
Journal of Vibration and Acoustics
1
Introduction
Air cleaners, having capacities for enclosed spaces of
25– 50 m2, are usually operated in quiet rooms for a long time in
the vicinity of people. For example, they operate for a whole day
or during working hours at homes or offices. Because the air
pollution levels are ever increasing in urban areas, the air-cleaner
quickly becomes an essential appliance for small offices and
homes. During the normal purification operation, the machine operational levels are low and people in the room usually expect a
calm and quiet, or even not audible, sound. When the air-cleaner
detects pollutants, the machine automatically changes its operation to a preset level depending on the extent of the pollution. At
high operational levels, contrastingly, a powerful and wellcleaning yet nonannoying and more undisturbing sound is desired,
where the auditory feeling is connected to an immediate and effective cleaning of pollutants. Considering the viewpoint of such
contradictory expectations and the demands of the customers,
sound quality should be investigated for the evaluation and design
of emitted noise. Usually, the fan noise is the main source of noise
in the mechanical-type cleaner using high efficiency particulate air
共HEPA兲 filters. Fan noise is unavoidable due to the circulation of
air; however, different from the air-conditioning system, direct
contact of the air stream with room occupants is not required in
the design. Owing to this feature, the designer has an opportunity
to modify the air-cleaner structure, mainly for the inlet and outlet
designs or the operational conditions of the fan. Because there has
been no sound quality model for the total perception of air-cleaner
sound, as far as to the authors’ knowledge, it was imperative to
find the relation between subjective feelings and physical nature
of the air-cleaner sounds. Three wide specific-loudness bands
were classified as representative sound bands based on the noise
characteristics of air-cleaners: 20–400 Hz 共0–3.8 barks兲, 400–
1250 Hz 共3.8–10 barks兲, and 1.25–12.5 kHz bands 共10–22.8
barks兲. Two kinds of subjective tests using the edited sounds were
conducted by the semantic differential method 共SDM兲 关1,2兴 and
the method of successive intervals 共MSI兲 关3兴. SDM tests for seven
adjective pairs describing the feeling to the air-cleaner sound were
conducted to find the relation between subjective feeling and frequency bands. Because we had many test data, the popular paired
comparison method 共PCM兲 关4兴 was not easy to use. In practicing
the MSI test, many sound samples edited from various operational
and artificial conditions are tested for a given question. In this
case, a score should be marked to each sound within a given scale,
and then the average of the repeated tests yields a result comparable to the PCM. Two major feelings were factored out from the
principal component analysis. Two indices, which were derived
for evaluating the two major feelings, were validated through the
other test set. A design guideline for air-cleaner sound is suggested.
2
Analysis of Air Cleaner Sound
The noise spectrum was measured in an anechoic chamber, for
which the measurement method complies with the Korean Standard, KS C 9314 关5兴. The radiated pressures in the frontal direction and upward direction were chosen as the data for preparing
the jury tests. This is because the frontal direction is the usual
direction of hearing the air-cleaner sound in daily life and the
sound pressure level 共SPL兲 is the loudest in the upward direction
due to the outlet location of typical compact air-cleaners. From the
A-weighted noise spectrum 关6兴 in Fig. 1, one can find that the
loudest frequency ranges are 200–300 Hz and 400–700 Hz. The
measurement was made at the center of an anechoic chamber
共cutoff frequency= 110 Hz兲. The low frequency range at 200–
300 Hz is related to the radiation from the frontal panel and cover
plate, as well as the exhaust port. The noise from the rotation of
fan and motor dominated the frequency range in 400–700 Hz.
Sound power level 共PWL兲 was measured according to ISO 3745
关7兴 in a semi-anechoic chamber. Measured sound power levels are
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40
Level 1
Level 2
Level 3
Level 4
Level 5
Background
noise
SPL (dBA)
30
20
10
0
Increasing
operation level
-10
100
1000
Frequency (Hz)
Fig. 1 A-weighted noise spectrum „SPL… measured in the frontal direction for varying operation levels. The curve with the
lowest level depicts the background noise in the anechoic
chamber.
shown in Fig. 2. The intensity level is quite high at the opening
between the frontal cover and the main body at 200 Hz band.
Beyond 630 Hz band, the sound intensity from the outlet part is
predominant, as shown in Fig. 3共b兲. According to the measurement, the entire frequency range is divided into three sections: the
50
45
PWL (dBA)
40
35
30
25
20
15
10
5
100 160 250 400 630 1000160025004000630010000
1/3 octave band center freq. (Hz)
Fig. 2 A-weighted sound power level „PWL… of an air-cleaner
varying the operation level: „— 䉳 —… background noise, „—䊏—…
level 1, „—쎲—… level 2, „—䉱—… level 3, „—䉲—… level 4, and
„—⽧—… level 5
low frequency range below 400 Hz 共3.8 barks兲, where the structure borne noise due to the vibration of the frontal panel is dominant; fan and motor noise dominates the midfrequency 共MF兲 noise
共400 Hz–1.25 kHz兲; the air borne noise from the outlet becomes
predominant beyond 1.25 kHz 共10 barks兲, with some contribution
from the motor and fan as well.
Objective SQ metrics 关8兴 such as Zwicker loudness from
ISO532B 共N兲, sharpness 共S兲, roughness 共R兲, and fluctuation
strength 共F兲 were calculated, and the result can be seen in Fig. 4.
They represent the subjective sensational quantities related to loud
feeling, shrill feeling, rough feeling, and beating feeling, respectively. Although there are other SQ metrics aside from these four
metrics, they were discriminated from the evaluation after the
preliminary test. All SQ metrics were calculated by using the SQTEAM software in KAIST, which is a dedicated in-house program
written for the SQ evaluation. Comparison of our SQ program
was made with three commercial software packages, which were
available in 2004. It was noted that the three commercial software
packages did not always produce the same metric value. We confirmed that the algorithms implemented in our SQ platform match
those in the majority of other softwares 关9兴.
3 Development of the Sound Quality Indices for Air
Cleaner Noise
Two kinds of subjective tests using the edited sounds were conducted by the semantic differential method and the method of
successive intervals. SDM tests for seven adjective pairs were
conducted to find the change of subjective feeling in terms of
frequency bands. The radiated noise of one air-cleaner was measured and edited by increasing or decreasing the loudness by 30%
at three wide specific-loudness bands. Seven adjective pairs are
listed in Table 1. A seven-scaled test was conducted for 25 people
who were educated for the hearing test. The tests were repeated
three times to check the consistency of subjects. Test results reveal
a correlation coefficient larger than 0.7, and then data were collected and analyzed statistically. In the statistical processing of
consistent data, analysis of variance 共ANOVA兲 was applied to
each evaluation item to investigate any confident difference
among 28 sounds. ANOVA results show clear differences among
sounds, which was checked by Tukey’s method 关10,11兴. The confidence level for each statistical processing was 95%.
Two major feelings, performance and annoyance, were factored
out by the principal component analysis 共PCA兲 on seven adjective
pairs. Feelings of well cleaning, loudness, and roughness were
grouped in factor 1 and the other feelings of abnormal, cheap, and
shrill into factor 2. Factor 1 is closely related to the “well-
65
64
63
62
61
60
59
58
57
56
55
54
53
(a)
(b)
Fig. 3 Measured sound intensity distribution in the 1/3-octave band: „a… 200 Hz band and „b… 630 Hz band
034502-2 / Vol. 131, JUNE 2009
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1.8
S (acum)
6
N (sone)
2.0
Front
Rear
Left
Right
Top
8
4
1.6
Front
Rear
Left
Right
Top
1.4
2
(a)
1.2
1
2
3
Operational level
4
5
(b)
1
2
3
4
5
3
4
5
Operational level
0.70
Front
Rear
Left
Right
Top
0.06
Front
Rear
Left
Right
Top
0.65
F (vacil)
R (asper)
0.08
0.04
0.60
0.02
0.00
(c)
1
2
3
Operational level
4
5
0.55
(d)
1
2
Operational level
Fig. 4 Calculated SQ metrics: „a… N, „b… S, „c… R, and „d… F
performance and annoyance, were modeled in terms of three objective SQ metrics 共N , S , F兲, because R was not influential to the
feelings.
It was found that if the squared term of N was included in the
regression model, a better fitting model to the overall feelings
共R2 = 0.98 for both annoyance and performance兲 was obtained than
the model employing only the first order loudness value:
-LF
共1兲 How annoying is the sound?
共2兲 How do you feel about the air purifying performance of the
air-cleaner?
Jury tests using seven-scales were conducted for young people
and repeated twice. Figure 6 shows the variation in the subjective
response to 50 sound samples concerning the two viewpoints of
SQ evaluation. One can find that the judgment on the performance
feeling was harder than the annoyance. A stepwise regression
method was employed to remove the variables having small contribution. By the stepwise regression, two air-cleaner SQ indices,
Loud
Quiet
Shrill 共sharp兲
Rough
Unpleasant
Cheap 共high and inharmonic
timbre兲
Well cleaning
Abnormal
Not shrill 共dull兲
Smooth
Pleasant
Expensive 共low and harmonic
timbre兲
Poor-cleaning
Normal
Journal of Vibration and Acoustics
+M F
+HF
+HF
Sound 3
Sound 4
-HF
-LF
-M F
Sound 2
Table 1 List of adjective pairs for the test using the SDM
共1a兲
Annoyance = − 4.53 + 1.61N − 0.08N2 + 1.79S
Factor2
(annoyance)
operating feeling” of the air-cleaner, whereas factor 2 is related to
the “disturbing feeling” or annoyance. Factor scores after the PCA
are plotted in Fig. 5, where sound numbers 共1–5兲 specify the noise
samples radiated from five different air-cleaners. Increase in
sound pressure, either at low frequencies 共+LF兲 or high frequencies 共+HF兲 or both, contributes to the high performance factor.
This means that a loud sound gives rise to a feeling of a welloperating machine. However, one should be careful about the fact
that, unfortunately, midfrequency and high frequency components
amplify the annoyance factor as well. Similarly, ⫺LF, ⫺MF, and
⫺HF denote the decrease in sound in the corresponding frequency
bands.
Another subjective test was conducted to get the sound quality
indices. We used 50 air-cleaner sounds at various operation conditions for five different commercialized models. The questions
given to the subjects were as follows.
-HF
+M F
-LF
+LF
-LF
+LF
-HF
-M F
+HF
+M F
Factor1
(Perform ance)
-M F
+M F
Sound 1
+LF
+HF
+LF
-M F
-HF
Fig. 5 Factor scores by the PCA: „䊏… sound 1, „䉱… sound 2,
„⽧… sound 3, and „쎲… sound 4. Two factors are classified: performance and annoyance feelings. Each factor was in seven
scales: −3 to +3.
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7
Subjective response
7
6
Annoyance
5
4
3
4
3
2
1
1
5
10
15
20
25
30
Sound number
35
40
45
50
Subjective score
Predicted score
5
(a)
10
15
20
25
30
Sound number
7
Subjective response
7
6
Performance
5
2
(a)
(b)
6
5
4
3
3
1
20
25
30
Sound number
35
40
45
50
50
4
1
15
45
5
2
10
40
Subjective score
Predicted score
6
2
5
35
5
(b)
10 15
20
25 30
Sound number
35
40 45
50
Fig. 6 Subjective response for the second experiment. „a… Annoyance and „b… performance. „쎲… All juries and „䊏… consistent
juries.
Fig. 7 A comparison of tested and predicted subjective
scores: „a… performance and „b… annoyance
Performance = − 2.58 + 1.53N − 0.09N2 − 0.87S + 4.64F
was decreased by 0.4 sone. In this case, a small gain in performance feeling was observed, but the annoyance feeling was reduced by about 1 subjective point. The edited sounds 3 and 4 were
all louder than the original one. However, annoyance evaluations
were equal to or less than that of sound 1 and performance values
were higher than that of the original sound. This is due to the fact
that the low frequency component is enhanced while the high
frequency components are suppressed in the modification of
sound 1.
共1b兲
Using Eq. 共1兲, the SQ of the measured data was evaluated and
compared with the subjective data. Test data were not included in
deriving Eq. 共1兲 and were obtained from different air-cleaners.
The difference between predicted and tested scores for 50 sample
sounds was found to be less than 0.5 points, as shown in Fig. 7.
Instead of using the model in Eq. 共1兲, SQ indices for evaluating
the air-cleaner noise could be made in terms of total loudness
values at low, mid-, and high frequency ranges by stepwise regression as follows 共R2 = 0.94 for annoyance and R2 = 0.90 for performance兲:
Annoyance = − 0.13 + 0.22Nlow
1.28Nmedium
freq.
共2a兲
freq.
Performance = 0.41 + 1.70Nlow
− 0.44Nhigh
freq. +
freq.
1.43Nmedium
freq.
共2b兲
In the derivation, each index was assumed to be proportional to
the first order quantity of band loudness values, which was based
on the observation of the application results of Eq. 共1兲. One can
find that all sound components contribute to the annoyance,
whereas the high frequency component has a negative effect on
the performance index. Because the just noticeable differences
共JNDs兲 for the N and S are known as 7% and 5% 关8兴, respectively,
the calculated JNDs of the performance and annoyance were
found to be 0.4 and 0.2, respectively.
Using the derived SQ indices, the sound spectrum could be
modified to suggest a desirable sound from the viewpoint of SQ.
Examples are shown in Fig. 8. The corresponding SQ indices
were calculated and listed in Table 2. Comparing sound 1 and
sound 2, overall SPL was increased by 2.5 dB, but the loudness
034502-4 / Vol. 131, JUNE 2009
40
SPL (dBA)
+ 0.65Nhigh
freq. +
50
30
20
10
125
200
315
500
800 1.25k
2k
3.15k
1/3 octave band center freq. (Hz)
5k
8k
Fig. 8 Spectra of several modified sounds: „—䊏—… sound 1
„original…, „—쎲—… sound 2 „+LF, ⴚMF, ⴚHF; ⌬L = 6 dB…, „—䉱—…
sound 3 „+LF, +MF, ⴚHF; ⌬L = 4 dB…, and „—䉲—… sound 4 „+LF,
ⴚHF; ⌬L = 6 dB…
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Table 2 Sound quality indices and sound pressure level, loudness of modified sound samples
in Fig. 8
Sample No.
Sound
Sound
Sound
Sound
4
1
2
3
4
Edited
frequency
ranges
Annoyance
共Eq. 共1a兲兲
Performance
共Eq. 共1b兲兲
SPL
共dBA兲
Loudness
共sone兲
Original
+LF, ⫺MF, ⫺HF
+LF, +MF, ⫺HF
+LF, ⫺HF
4.3
3.4
4.3
3.8
5.4
5.6
6.4
5.9
49.0
51.5
54.5
52.5
5.9
5.5
7.3
6.2
Conclusions
In this study, the SQ indices for the noise from a mechanicaltype air-cleaner were developed. To this end, the frequency range
was divided into three sectors based on the noise characteristics:
low frequency 共20–400 Hz兲, midfrequency 共400–1250 Hz兲, and
high frequency 共beyond 1.25 kHz兲. As a result of the subjective
evaluation, two principal components, performance and annoyance, were extracted as main perceptions on the air-cleaner noise.
We found that the low frequency components enhance the feeling
of strong performance. High frequency components can enhance
the performance index but have the adverse effect of increasing
the annoyance. According to the MSI test, two SQ indices, performance and annoyance, were modeled in terms of objective SQ
metrics, N, S, and F. The results of this study would be useful in
the noise control and psychoacoustic design of radiated sounds
from an air-cleaner.
Acknowledgment
This work was partially supported by the BK21 Program. The
authors would like to thank Woongjin Coway Co., Ltd. for their
support.
Nomenclature
F ⫽ fluctuation strength
N ⫽ loudness
Journal of Vibration and Acoustics
R ⫽ roughness
S ⫽ sharpness
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