SENSORY EVALUATION OF FABRIC SOUND BY FREE MODULUS
MAGNITUDE ESTIMATION
Gilsoo Cho1, Eunjou Yi.1 and John G. Casali2
1
Department of Clothing and Textiles, College of Human Ecology, Yonsei University,
134 Shinchon-Dong, Sudaemoon-Gu, Seoul, 120-749, Korea
(corresponding author - Emal: [email protected])
2
Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA. 24061,
U.S.A.
ABSTRACT
This study was performed to measure fabric sound objectively and to relate the sound with
human subjective sensation. Rubbing sound of each fabric was generated by an apparatus
devised for this study and transformed into sound spectrum by fast fourier transform (FFT)
analysis. As sound characteristics, level pressure of total sound (LPT), amplitude difference
(∆L), and frequency difference (∆f) were obtained. Seven sensation (softness, loudness,
pleasantness, sharpness, clearness, roughness, and highness) and satisfaction for sound of
each fabric were rated by free modulus magnitude estimation (FMME). The LPT of
polyester taffeta was the highest (62.1dB) while the value of polyester ultrasuede
was the lowest (37.4dB) among the fabrics. Values for level range (∆L) of eight fabrics
were ranged from 18.6dB to 52.4dB. Frequency difference (∆f) of all fabrics had negative
values. Eight fabrics showed significant differences one another in each of sound sensation.
Among the sensation, loudness, sharpness, roughness and highness were negatively
correlated with sensation of softness and pleasantness. A regression model of each sensation
was fitted as a function of sound parameters. Loudness, roughness, and highness were well
predicted by sound parameters showing R2 higher than 0.7.
Keyword : fabric sound, level pressure of total sound (LPT), level difference (∆L), frequency
difference (∆f), sensation, free modulus magnitude estimation (FMME)
1.
INTRODUCTION
The evaluation of fabric hand, quality, and related fabric performance attributes, in terms of
objectively measurable properties is called Fabric Objective Measurement (FOM) (Bishop,
1996). The successful application of FOM depends on establishing reliable methods for
quantifying subjective judgments, and on providing equations that accurately predict such
judgments from the chosen objective measurements.
Fabric sensory properties such as tactility, drape, luster, hairiness, prickle, and odor have been
discussed in the literatures. However, there has been no publication on fabric sound in the
present FOM context. There were some publications dealing with fabric sound measurements
by now. Two edges of a micro-slit of a trilobal shaped cross-section were applied to a polyester fiber
in order to imitate silk-scrooping (Fukuhara, 1993). In another study, relationship between sound
parameters objectively measured and some mechanical properties of fabrics was discussed
(Yi and Cho, 1999). However, there has been no study which measured human sensation of
fabric sound.
RJTA Vol. 5 No. 2
29
Sound sensation is affected by the related physical sound parameters. Subjective attributes for
sound sensation are loudness, pitch, timber and duration. These are determined by physical
parameters such as sound pressure, frequency, spectral shape and so on (Sethares, 1998).
Human sensation has been evaluated by psychophysical methods such as semantic
differential scaling (SDS) and magnitude estimation (Bird and Noma, 1978). Magnitude
estimation is a method to evaluate sensation against stimuli by giving a number which equals
to the magnitude of perceived attribute (Schiffman, 1971). In textile fields, magnitude
estimation has been used in some studies such as that subjective hand properties including
softness, smoothness, and flexibility were predicted with some relevant objective
measurements (Jeurissen, 1991). There are two different ways of magnitude estimation. One
is a fixed modulus magnitude estimation and the other is free modulus magnitude estimation
(FMME). In the fixed modulus magnitude estimation, subjects decide their magnitude against
stimuli according to the predetermined standard, whereas in the FMME, subjects freely give a
number to the stimuli without predetermined standard by considering each stimulus’s
intervals. According to Stevens (1971), FMME is a better method for each subject to decide
his or her own modulus on the sensation estimation than fixed modulus.
The purpose of this study is to measure fabric sound objectively based on FFT spectra and to
relate the sound with human subjective sensation so that human sensation for fabric sound
can be predicted from the objectively measurable sound parameters of fabrics. It is
anticipated that the results will be utilized as a source to develop textile fabrics that will
satisfy consumers in terms of auditory sensation.
2.
EXPERIMENTAL METHOD AND ANALYSIS
2.1
Specimen
Eight different fabrics were used as test specimen. The characteristics of the specimen were
summarized in Table 1.
Table 1. Characteristics of Fabrics
Fabric
Number
F1
F2
F3
F4
F5
F6
F7
F8
2.2
Fiber
Content
Wool
Wool
Polyester
Polyester
Silk
Polyester
Polyester
Flax
Yarn
Type
Staple
Staple
Staple
Staple
Filament
Filament
Filament
Staple
Fabric Construction/
Name
Plain/Worsted
Plain/Woolen
Twill/Ultrasuede
Leno/Leno
Plain/Crepe de chine
Twill/Surah
Plain/Taffeta
Plain/Beaten
Thickness
(mm)
0.46
0.68
0.30
0.58
0.18
0.25
0.28
0.40
Weight
(mg/cm2)
23.24
40.52
14.28
22.34
6.51
12.91
16.35
15.95
Sound Recording
An apparatus devised to generate fabric sounds for the previous study (Yi and Cho, 1999)
was used (Fig. 1). Two pieces of same fabrics were faced to each other on the apparatus. One
was fixed on a stationary pulley and the other was placed on it like a belt. The upper fabric
RJTA Vol. 5 No. 2
30
was then moved and rubbed to the other fabric so as to generate rustling sounds. The feeding
speed of specimen was controlled to be 1.2m/min.. The apparatus did not make any noise. It
simulated the actual rubbing of garments in real state. Sound generating was repeatable and
reproducible by use of the apparatus. The weight on the specimen was adjusted to 500g /4 cm.
The loads were used to result movement of the upper fabric. In the air cylinder airflow occurs
resulting in energy loss to keep the velocity of feeding speed from accelerating.
Each sound was recorded in an anechoic chamber by use of a microphone (B & K, Type
4145) and a DAT data recorder (TEAC Model RD-145T). The use of DAT allowed the
recordings to be played during experimental trials while holding accurate calibration levels.
This apparatus afforded the ability to replicate rubbing conditions of the same fabrics,
allowing accurate recording to be done.
2.3
Analysis
Recorded sounds were analyzed in terms of amplitude and frequency by an FFT analyzer
(Hewlett Packard, model 35670A). Spectra of sounds were output from the analyzer.
Fig. 1. Diagram of Sound Generator
A1 :
A2 :
B:
C:
D:
2.4
Piston
Pipe
Cylinder
Valve
Load (500g)
E:
F1 :
F2 :
G:
H:
Load (5kg)
Fabric (15 cm width×75 cm length)
Fabric wounded on pulley (15 cm width×15 cm length)
Microphone
Holder
Definition and Measurement of Sound Parameters
In order to summarize the fabric sound expressed as FFT spectra, three sound parameters
were defined which can describe the characteristics of the sound well. Those are the level
pressure of total sound(LPT), level range(∆L), and frequency difference(∆f). The three
parameters were calculated from the spectral curves as follows;
LPT = 10 log 10 ( ∑ 10
BL i
10
(1)
)
i
where BLi is i-th broadband level .
RJTA Vol. 5 No. 2
31
∆L (dB) = (maximum amplitude) – (minimum amplitude)
(2)
∆f (Hz) = (frequency at maximum amplitude) – (frequency at minimum amplitude) (3)
2.5
Measurement of mechanical Properties of Fabrics
Mechanical properties of eight fabrics were measured by using KES-FB.
2.6
Subjects
Participants for this study were recruited from the Virginia Tech student population by means
of posted fliers and postings to the local VT newsgroups. A total of 30 subjects (14 male
students, 16 female students) between 18 and 26 years of age participated in the study after
screening for normal hearing of volunteers.
2.7
Screening
The screening was consisted of a hearing test and several questions to assess the general
health and condition of the subjects’ ears. The participant’s auditory threshold was
determined according to 5dB up and 10dB down procedure (Morill, 1984) by use of an
audiometer, and was required to be at least 20dB at 500, 1000, 2000, 3000 and 4000 Hz
frequencies.
2.8
Sensory Evaluation
A set of prerecorded fabric sounds was presented to each subject. For each sound, the
subjects were asked to answer questions on their subjective sensation of the sound. The
questions dealt with seven aspects of sound sensation: softness (S1), loudness (S2),
pleasantness (S3), sharpness (S4), clearness (S5), roughness (S6), highness (S7), and
satisfaction. The sensory descriptors were selected through the pretest in which subjects were
asked to choose sensation terms available for describing fabric sound. The questionnaire was
structured in the form of FMME. In each sound presentation, each subject assigns a number
to each of seven sensation and satisfaction so that high number represents high sensation and
low number represents low sensation (Cho and Casali, 1999).
Sensory measurements were repeated twice so that two responses are obtained for each
subject and each sensation. Each subject was presented with eight different sounds of fabrics
in sequence. The order of sound presentation was previously determined using random
number table for each subject.
2.9
Steps for FMME Data Transform
To compare magnitude estimation judgments among subjects who responded using varied
number ranges, every score for each subject required correction. The subjective FMME data
were transformed (Haas, 1993) to eliminate inter-subject variance and intra-subject
variability as follows:
RJTA Vol. 5 No. 2
32
c Convert each response value to its logarithm.
d Calculate the arithmetic mean of the logarithms of the two responses made by each
observer to each sensation. This value is equal to the logarithm of the geometric mean of
the observer’s responses to each sensation.
e Tabulate the values such that subjects are listed by row, and sensation is listed by
column.
f Calculate the arithmetic mean of the logarithmic responses in each row. This is equal to
the logarithm of the geometric mean of each observer’s responses to all the sensation.
g Obtain the arithmetic mean of all the values obtained in step f. This is equal to the
logarithmic value of the grand mean of all the responses for all observers to all sensation in
the original data matrix.
h Subtract the value obtained in step g, the grand mean log response, from each of the
arithmetic individual mean log responses determined in step f.
i Subtract the value obtained in step h from the row of values obtained for each subject
in step d.
j Obtain the antilog of every value obtained in step i.
3.
RESULTS AND DISCUSSION
3.1
Sound Properties of Fabrics
Sound parameters as physical stimuli of fabrics for human sensation were evaluated using the
equations (1)-(3). The results are given in Fig.2. The LPT of F7 (polyester taffeta) was the
highest (62.1dB). This corresponds to the sound pressure level of normal
conversation (60~70dB). The value of F3 (polyester ultra suede) was the lowest
(37.4dB). This is the level of a living room (30~40dB). The wool fabrics (F1, F2)
showed similar LPT values, but worsted fabric (54.7dB) produced a little louder rustling
sound than woolen fabric (52.6dB). The LPT values of F4 (polyester leno) and F6 (polyester
surah) were comparatively high and those were higher than 50dB. The LPT of silk satin (F5)
was 49.3dB, which was a little louder than that of flax beaten fabric (F8) (46.8dB).
Level range (∆L) of eight fabrics was ranged from 18.6dB to 52.4dB. The higher the values,
the higher the dynamic ranges of the spectrum. Silk satin (F5) had the highest ∆L value of
52.4dB among eight fabrics, and polyester leno (F4) had the lowest ∆L of 8.6dB. The wool
fabrics (F1, F2) also showed very similar ∆L values (21.4dB and 23.0dB).
Frequency difference ( ∆f) of fabrics had negative value. A negative value is a characteristic
of a fabric with a lower frequency bias or a negative spectral slope. The higher the values, the
wider the spectrum. Wool worsted fabric (F1) had a narrow spectrum compared to woolen
fabric (F2). Leno fabric (F4) had the lowest value of ∆f and woolen fabric (F2), polyester
surah (F6) and flax (F8) had the highest.
RJTA Vol. 5 No. 2
33
Fig. 2. Quantified Sound Parameter Values of Eight Fabrics
70
70
60
60
50
50
30
0
-5000
40
delta
40
30
20
20
10
10
-15000
-25000
F1 F2 F3 F4 F5 F6 F7 F8
F1 F2 F3 F4 F5 F6 F7 F8
3.2
-10000
-20000
0
0
Frequency Difference
(delta f)
Am plitude Difference
(delta L)
delta L
LPT
LevelPressure of Total
Sound (LPT)
F1 F2 F3 F4 F5 F6 F7 F8
Mechanical Properties of Fabrics
Mechanical property measurements by KES are shown in Table 2. For shear stiffness (G) and
shear hysteresis (2HG), F5 (crepe de chine) showed the lowest value. It means that fabric was
more easily deformable than any other fabrics. Woolen fabric (F2) was found to be the
bulkiest and heaviest fabric showing the highest values for compression energy (WC) and
weight. Polyester leno (F4) was most stretchable in tensile strain because the fabric showed
the highest tensile energy (WT).
Table 2. Mechanical Property Measurements of Fabrics
EM
LT
(%)
F1
F2
F3
F4
F5
F6
F7
F8
3.3
6.46
8.82
3.81
10.82
13.07
3.44
4.00
2.20
0.63
0.58
0.76
0.71
0.48
0.73
0.65
0.80
WT
(gf.cm/cm2)
0.07
13.13
9.11
19.11
15.61
6.25
6.45
4.34
RT
G
2HG
2HG5
(%)
(gf/cm.deg)
(gf/cm)
(gf/cm)
(gf.cm/cm2)
WC
(mm)
T
(mg/cm2)
W
60.06
56.07
58.29
45.43
50.65
55.90
64.52
43.50
0.56
0.94
0.39
0.32
0.21
0.31
0.87
0.59
0.81
2.19
0.73
0.45
0.05
0.51
1.59
0.65
1.50
3.16
1.49
0.89
0.22
1.19
2.54
2.93
0.20
1.82
0.12
0.24
0.08
0.11
0.07
0.18
0.46
0.68
0.30
0.58
0.18
0.25
0.28
0.40
23.24
40.52
14.28
22.34
6.51
12.91
16.35
15.95
Sensory Properties of Fabric Sound
The means of subjective sensation by FMME for eight fabrics are shown in Fig. 3. The oneway analysis of variance (ANOVA) was performed for each of seven sensation in order to
test whether sensation effects of eight fabrics are the same or not. All sound sensation was
significantly different at 1% significant level among eight fabrics. P-values were 0.0046 for
clearness (S5) and 0.0001 for all the other sensations. Polyester ultrasuede (F3) with the
lowest LPT showed the highest sensation for softness (S1) and pleasantness (S3), and the
lowest for loudness (S2), sharpness (S4), roughness (S6) and highness (S7). Polyester taffeta
(F7) with the highest LPT showed the highest sensation for loudness (S2), sharpness (S4),
roughness (S6) and highness (S7), and the lowest for softness (S1) and pleasantness (S3).
Fabrics evaluated as louder, sharper, rougher and higher showed smaller for softness (S1),
and pleasantness (S3).
RJTA Vol. 5 No. 2
34
Fig. 3. Means of Sound Sensation of Fabrics
(P-value of * is 0.0046, others are 0.0001)
S oftness (S 1)
Loudness (S 2)
P leasantness (S 3)
S harpness (S 4)
12
12
12
12
10
10
10
10
8
8
8
8
6
6
6
6
4
4
4
4
2
2
2
2
0
0
0
F1 F2 F3 F4 F5 F6 F7 F8
C learness* (S 5)
R oughness (S 6)
F1 F2 F3 F4 F5 F6 F7 F8
H ighness (S 7)
12
12
12
10
10
10
8
8
8
6
6
6
4
4
4
2
2
2
0
0
F1 F2 F3 F4 F5 F6 F7 F8
0
F1 F2 F3 F4 F5 F6 F7 F8
F1 F2 F3 F4 F5 F6 F7 F8
0
F1 F2 F3 F4 F5 F6 F7 F8
F1 F2 F3 F4 F5 F6 F7 F8
In Table 3, correlation of the seven sensation is listed. From the table it is seen that the four
sensation of loudness (S2), sharpness (S4), roughness (S6), and highness (S7) were positively
correlated with each other and the sensation of softness (S1) and pleasantness (S3) was
positively correlated with each other. On the other hand, each of the four sensation (S2, S4,
S6, S7) was negatively correlated with each of the two sensation (S1, S3). Clearness is the
only sensation which was not significantly correlated with any of the other sensation.
Table 3. Correlation Coefficients among sensation
S1
S3
S5
S2
S4
S6
S7
S1
1.000
S3
0.710
1.000
S5
0.091
0.006
1.000
S2
-0.683
-0.758
0.115
1.000
S4
-0.526
-0.634
0.217
0.725
1.000
S6
-0.661
-0.790
0.066
0.837
0.731
1.000
S7
-0.612
-0.735
0.130
0.819
0.783
0.814
1.000
RJTA Vol. 5 No. 2
35
Satisfaction of fabrics is shown in Fig. 4. Participants rated polyester ultrasuede fabric as the
most satisfied one and the next ones were silk satin and flax fabrics. Those three fabrics had
been rated as most soft and pleasant in the softness and pleasantness sensation. Polyester
taffeta was the least satisfied fabric for its sound. It had been rated as the loudest, sharpest,
roughest and highest in their sensation. Therefore, satisfaction was positively correlated with
sensation of softness and pleasantness, while negatively with sensation of loudness, sharpness,
roughness and highness.
Fig. 4. Means of Satisfaction of Fabrics
S a tis fa c tio n
12
10
8
6
4
2
0
F1 F2 F3
3.4
F4 F5 F6 F7 F8
Prediction of Sound Sensation by Sound Parameters
Before establishing the prediction models, significance of correlation between sound
parameters and sound sensation of fabrics was presented in Table 4. LPT showed significant
correlation coefficients with all of sensation except subjective sensation. It was correlated
negatively with softness and pleasantness, while positively with loudness, sharpness,
roughness, and highness. The other hand, ∆L had significant correlation positively with
softness, while negatively with loudness, sharpness, and highness. Clearness did not show
any significant correlation with sound sensation, which means that other sound parameters
need to be investigated for correlation with sound sensation of fabrics.
Table 4. Correlation Coefficients between Sound Parameters and Sound Sensation
Softness
Loudness
Pleasantness
Sharpness
Clearness
Roughness
Highness
LPT (dB)
- 0.863**
0.864**
- 0.902**
0.764*
0.085
0.915**
0.757*
∆L
∆f
0.757*
- 0.779*
0.699
- 0.863**
- 0.442
- 0.692
- 0.858**
** means p<0.01, *means p<0.05
RJTA Vol. 5 No. 2
36
0.061
- 0.005
- 0.027
0.097
- 0.270
- 0.010
0.152
A multiple linear regression model was fitted to each of the sound sensation as a function of
the three sound parameters and their two-way cross products, which is
S = b0 + b1·∆L + b2· ∆f + b3·LPT + b4·∆L·∆f + b5·∆L·LPT + b6·∆f ·LPT
where S denotes the sound sensation and b0- b6 are coefficients of the regression model. The
squared terms of the sound parameters were not included in the regression model since none
of them were significant in the model. The coefficients b0- b6 and coefficient of determination
(R2) for the seven sensation by FMME are listed in Table 5.
Table 5. Prediction of Sound Sensation
Sound
Sensation
Softness (S1)
Loudness (S2)
Pleasantness (S3)
Sharpness (S4)
Clearness (S5)
Roughness (S6)
Highness (S7)
b0
-2.64
2.41
-0.60
6.21
11.81
-6.90
13.73
b1
0.56
-0.15
0.54
-0.20
-0.20
0.09
-0.35
Coefficients
b2
b3
0.00
0.11
0.00
0.16
-0.00
0.08
-0.00
0.07
-0.00
-0.09
0.00
0.33
0.00
-0.06
b4
-0.00
0.00
-0.00
0.00
-0.00
0.00
0.00
b5
-0.01
0.00
-0.01
0.00
0.00
-0.00
0.00
R2
b6
0.00
-0.00
0.00
-0.00
0.00
-0.00
-0.00
0.53
0.84
0.64
0.52
0.08
0.73
0.73
Loudness, roughness, and highness were well predicted by sound parameters and R2 of the
models were higher than 0.7. Softness, pleasantness, sharpness had relatively low R2 and
clearness had very small R2. In the equations, subjective loudness was found as being related
positively with LPT. Fig. 5 illustrates the relation between LPT and loudness based on
psychophysical law. The figure explains that fabrics with higher LPT tended to sound loudly.
The prediction equation of roughness also had a positive coefficient for LPT. The relationship
between them is shown in Fig. 6. The other hand, subjective highness was affected negatively
by ∆L in the equation. This means that fabrics with lower values for ∆L seemed to be rated as
sounding higher, which is presented in Fig. 7.
RJTA Vol. 5 No. 2
37
Fig. 5. Relationship between Loudness and LPT
Loudness = – 0.003 LPT2 + 0.577 LPT -15.989, R2 = 0.75
Loudness (scales)
12
F7
10
F2 F1
F4
F6
8
6
F8
4
2
30
F5
F3
40
50
60
70
LPT (dB)
Fig. 6. Relationship between Roughness and LPT
Roughness = 0.001 LPT2 + 0.274LPT – 9.059, R2 = 0.84
Roughness (scales)
12
F7
10
F2
F4 F1
F6
8
6
F8
F5
4
F3
2
30
40
50
LPT (dB)
RJTA Vol. 5 No. 2
38
60
70
Fig. 7. Relationship between Highness and ∆L
Highness = 0.004 ∆L 2 – 0.493 ∆L +17.103, R2 = 0.77
Highness (scales)
10
9
8
F4
F7
F2
F1
F6
7
6
5
F8
4
3
2
10
F3
20
30
40
F5
50
60
delta L (dB)
3.5
Sound Sensation and Satisfaction Predicted by Sound and Mechanical Measurements
Table 6 presents the equations for predicting sound sensation with sound parameters and
mechanical properties of fabrics. Softness sensation was predicted by LPT, elongation (EM),
shear hysteresis (2HG), compression energy (WC), and fabric thickness (T). The perception
of roughness for sound was adequately predicted by ∆f, LPT, tensile resilience (RT), shear
stiffness (G), and thickness. Sound sensation was well predicted with sound parameters and
physical properties by FMME. For most of the equations R2 was higher than 0.9. This
indicates that subjective evaluation by FMME contributed on establishing reliable equations
for describing sound sensation with sound and physical properties.
The equations for predicting satisfaction for sound showed that satisfaction for sound was
well described by ∆L, LPT, and fabric thickness in sound measurement and mechanical
properties and also described by loud sensation.
Table 6. Regression Equations for Sound Sensation and Satisfaction Predicted by both Sound
Parameters and Mechanical Properties
Sound Sensation
Softness
Loudness
Pleasantness
Sharpness
Clearness
Roughness
Highness
Satisfaction
Regression Equation
Y =23.873-0.287LPT +0.032EM-0.1682HG +1.050WC-8.328T
Y =-8.847-0.050∆L+0.295LPT +5.109T
Y =32.599-0.001∆f-1.343WT
Y =-4.181+0.00005∆f +0.128LPT-4.633G +12.782T
Y = 4.566-0.0001∆f +0.8602HG5
Y =-19.796+0.0001∆f +0.264LPT+0.163RT -4.904G+22.022T
Y =-2.335-0.138∆L+0.249LPT
Y =18.914 + 0.048 ∆L - 0.250 LPT - 4.366T
R2
0.999
0.983
0.780
0.986
0.709
1.000
0.977
0.988
RJTA Vol. 5 No. 2
39
4.
CONCLUSIONS
Sound parameters as physical stimuli of fabrics for human sensation were quantified using
the equations. The means for subjective sensation by FMME for each fabric showed that all
sound sensation were significantly different for eight fabrics. Sensation of loudness,
sharpness, roughness and highness was negatively correlated with sensation of softness and
pleasantness. For sensation measurements, semantic differential scales (SDS) could be
utilized in order to compare with the results obtained from the FMME.
Sound sensation was well predicted with sound parameters and physical properties showing
the high R2. This indicates that subjective evaluation by FMME contributed on establishing
reliable equations for describing sound sensation with sound and physical properties.
REFERENCES
Bird, J.C. and Noma, E. (1978), Fundamentals of Scaling and Psychophysics, John Wiley &
Sons, Inc., New York.
Bishop, D.P. (1996), Fabrics: Sensory and Mechanical Properties, Textile Progress, 26, pp. 157.
Cho, G. and Casali, J.G. (1999), Sensory Evaluation of Fabric Sound and Touch by Free
Modulus Magnitude Estimation, the 5th proceedings of Asian Textile Conferences, pp. 307310.
Fukuhara, M. (1993), Innovation in Polyester Fibers: From Silk-Like to New Polyester,
Textile Research Journal, 63, pp. 387-391.
Haas, E.C. (1993), The Perceived Urgency and Detection Time of Multitone and FrequencyModulated Warning Signals in Broadband noise, Doctorial Dissertation, Virginia Tech.
Jeurissen, P.C J. (1991), Looking for a Relation between Sensory and Instrumental Data, final
report of the postgraduate program ‘Mathematics for Industry’, Technische Universiteit
Eindhoven, the Netherlands, ISBN 90-5282-118-6 bound.
Morill, J.C. (1984), Audiometric Instrumentation: Equipment choices Technique,
Occupational Health and Safety, 53(10), pp. 78-84.
Schiffman, H.R. (1976), Sensation and Perception, John Wiley & Sons, Inc., New York.
Sethares, W.A. (1998), Tuning, Timber, Spectrum, Scale, Springer-Verlag London Limited.
Stevens, S.S. (1971), Issues in Psychophysical Measurement, Psychophysical Review, 78(5),
pp. 426-450.
Yi, E., and Cho, G. (1999), Relationship between Characteristic Parameters of Rustling
Sounds and Mechanical Properties of Fabrics, J. of Korean Fiber Society, 36(5), pp. 403-410.
RJTA Vol. 5 No. 2
40