concert hall acoustics

97 Pages·2001·15.99 MB·English

Orthogonal Factors Describing Primary and Spatial
Sensations of the Sound Field in a Concert Hall

Yoichi Ando

Graduate School of Science and Technology, Kobe University
Rokkodai, Nada, Kobe 657-8501 Japan
Subjective preference of the sound field in a concert hall is described based on the model of human auditory-brain system. The
model consists of the autocorrelation function (ACF) mechanism and the interaural crosscorrelation function (IACF)
mechanism for signals arriving at two ear entrances, and the specialization of human cerebral hemispheres [Ando, Architectural
Acoustics, AIP/Springer, 1998]. From this view point, primary sensations such as pitch or missing fundamental, loudness,
timbre, and in addition duration sensation which is introduce here as a fourth are well described by the temporal factors
extracted from the ACF associated with left hemisphere. And, spatial sensations such as apparent source width (ASW) and
subjective diffuseness are described by the spatial factors extracted from the IACF associated with the right hemisphere

ORTHOGONAL FACTORS peak. Usually, there are certain correlation between
τn and τn +1, and between φn and φn+1;
Primary sensations and spatial sensations as well as (3) Effective duration of the envelope of the
subjective preference for sound fields are well normalized ACF, τ, which is defined by the ten-
e
described by a model of the auditory-brain system. The percentile delay and which represents a repetitive
model includes autocorrelation function (ACF) and feature or reverberation containing the sound
interaural crosscorrelation function (IACF) source it.
mechanisms [1,2]. Important evidences supporting this
(a) 1
model were discovered in relation to the auditory-brain
activity [2]. This article reviews that primary
φ
sensations and spatial sensations are mainly described
1
by temporal and spatial factors extracted from the ACF
τ)
and the IACF, respectively. φ(p 0

τ
Factors extracted from the ACF
1

-1
The ACF is defined by 0
τ
Delay time 　[ms]
+T
Φp(τ) = 1 p'(t)p'(t+ τ)dt (1) (b) 0 τ
2T e
-T
]
B
d
[ -5

where p’(t) = p(t)*s(t), s(t) being the ear sensitivity,
which is essentially formed by the transfer function of τ) |
(
physical system to oval of cochlea. For convenience, φp-10
s(t) may be chosen as the impulse response of an A- g |
o
weighted network [1,2]. The ACF and the power l
density spectrum contain the same information. There -15
are four factors, which can be extracted from the ACF: 0 100 200
Delay time τ　[ms]
(1) Energy represented at the origin of the delay, Φ (0);
p FIGURE 1. Definition of independent factors other than
(2) Fine structure, including peaks and delays (Figure Φ(0) extracted from the normalized ACF. (a) Values of
1a). For instance, τ1 and φ1 are the delay time and τ1 and φ1 for the first peak; (b) The effective duration of
the amplitude of the first peak of ACF, τn and φn the ACF τe is obtained practically by the extrapolation
being the delay time and the amplitude of the n-th of the envelope of the normalized ACF during the decay,

5 dB initial (b).
The normalized ACF is defined by

φ (τ) = Φ (τ) /Φ (0) (2)
p p p

As a manner shown in Figure 1b, the value of τ is
e
obtained by fitting a straight line for extrapolation of
delay time at –10 dB, if the initial envelope of ACF
decays exponentially. Therefore, four orthogonal and
temporal factors that can be extracted from the ACF
are ΦΦΦΦ (0), ττττ, φφφφ , and ττττ .
p 1 1 e

Auditory-Temporal Window

In analysis of the running ACF, of particular interest FIGURE 2. Definition of independent factors IACC,

is so called an “auditory-temporal window”, 2T in τ and W extracted from the normalized IACF.
IACC IACC
Equation (1), that must be determined. Since the initial
part of ACF within the effective duration τ of the ACF
e
contains the most important information of the signal, PRIMARY SENSATIONS
thus the recommended signal duration (2T) is given
r
by
Loudness

(2T) ≈ K (τ) [s] (3)
r 1 e min
Let us now consider primary sensations. Loudness s
L
where (τ) is the minimum value of τ obtained by is given by
e min e
analyzing the running ACF, K being the constant
1 s = f[ΦΦΦΦ (0), ττττ, φ , ττττ, D] (6)
around 30 [7]. The running step (R) is selected as L p 1 1 e
s

K (2T), K being selected, say, in the range of 1/4 –
2 r 2
3/4. where D is the duration of sound signal as is
represented by musical notes. It is worth noticing that
Factors extracted from the IACF the value of τ corresponds to pitch of sound and/or the
1
missing fundamental as discussed below. Since the
The IACF is given by sampling frequency of the sound wave is more than the
twice of the maximum audio frequency, the value
+T 10logΦ (0)/Φ (0) is far more accurate than the L
ref eq
Φlr(τ) = 1 p'l(t)p'r(t+τ)dt (4) which is measured by the sound level meter.
2T Scale values of loudness within the critical band
-T
were obtained in paired-comparison tests (with filters

with the slope of 1080 or 2068 dB/octave) under the
where p’ (t) = p(t) *s(t), p(t) being the sound
pressure la t, r the left- l,rand right-le,rar entrances. The condition of a constant Φp(0) [2,4]. Obviously, when
τ
normalized IACF is given by sound signal has the similar repetitive feature,
e
becomes a great value, as like a pure tone, then the
φ (τ) = Φ (τ)/[Φ (0)Φ (0)]1/2 (5) greater loudness results. Thus a plot of loudness versus
lr lr ll rr
bandwidth is not flat in the critical band. This
where Φ (0) and Φ (0) are autocorrelation functions contradicts previous results of the frequency range
ll rr
(τ = 0) or sound energies arriving at the left- and right- centered on 1 kHz [5].
ear entrance, respectively. Spatial factors extracted
from the IACF are defined in Figure 2 [2]. Pitch
In analyzing the running IACF, 2T is selected by
Equation (3) also. For the purpose of spatial design for The second primary sensation applying the ACF is
sound fields, however, longer values of (2T) may be the pitch or the missing fundamental of the noise. It is
r
useful, because it is essentially time independent. given by

s = f[Φ (0), ττττ, φφφφ , τ , D] (7)
P p 1 1 e
When a sound signal contains only a number of Table 1. Primary sensations in relation to factors extracted
harmonics without the fundamental frequency, we hear from the autocorrelation function and the interaural
crosscorrelationfunction.
the fundamental as a pitch. This phenomenon is well
explained by the delay time of the first peak in the
ACF fine structure, τ [6,7]. According to experimental Factors Primitive Sensations
1
results on the pitch perceived when listening to
bandpass noises without any fundamental frequency, Loudness Pitch Timbrea) Duration
the pitch s is expressed by equation (7) as well, under
p
the condition of a constant Φπ(0). The strength of the ACF Φ (o) X x X X
pitch sensation is described by the magnitude of the p
τ X X X X
first peak of the ACF, φ1. For a signal of short duration, φ1 x X X X
factor D must be taken into account. τ1 X x X x
e
D xb) xb) Xb) X
Timbre

The third primary sensation, timbre that includes X and x : Major and minor factors influencing the corresponding
response, respectively.
pitch, loudness, and duration, might be expressed by
a). Timbre in relation to all of temporal and spatial factors is under
investigation.
s = f[ΦΦΦΦ (0),ττττ, ττττ, φφφφ , D] (8) b). It is suggested that loudness, pitch and timbre should be
T p e 1 1
examined in relation to the signal duration.

It is worth noticing that the intelligibility of single
s = f[LL, IACC, ττττ , W ] (10)
syllables as a function of the delay time of single IACC IACC

reflection is well be calculated by the four orthogonal
where
factors extracted from the running ACF analyzed for

the piece between consonant and vowel sounds [7]. A
LL = 10 log [Φ (0)/Φ(0) ] (11)
recent investigation, clearly show that timbre or p ref

dissimilarity judgment is an overall subjective response
similar for the subjective preference of sound fields in And Φp(0) = [Φll(0) Φrr(0)]1/2, and Φll(0) and Φrr(0)
concert hall. being ACFs at τ = 0 (sound energies), of the signals
arriving at the left and right ear-entrances. In four
orthogonal factors in Equation (10), the interaural
Duration
delay time, τ , is a significant factor in determining
IACC
the perceived horizontal direction of the source. A
The forth-primitive sensation, which is introduced
well-defined direction is perceived when the
here, is the perception of signal duration, which is
normalized interaural crosscorrelation function has one
given by [12,13]
sharp maximum, a high value of the IACC and a

narrow value of the W , due to high frequency
s = f[ΦΦΦΦ (0), ττττ, φφφφ , τ, D] (9) IACC
D p 1 1 e components. On the other hand, subjective diffuseness

or no spatial directional impression corresponds to a
One of experimental results has been expressed in
low value of IACC (< 0.15) [9].
relation to τ , φ , and D [8]. Table 1 indicates
1 1 Of particular interest is that, for the perception of a
summarization of primary sensations in relation to
sound source located in the median plane, the temporal
factors extracted from the ACF and physical signal
factors extracted from the ACF of sound signal
duration D.
arriving at the ear-entrances may act as cues . It has
been shown that three factors, τ, τ , and φ as a
SPATIAL SENSATIONS e 1 1
function of the incident angle greatly differ, but few
differences may be found in the head-related transfer
Directional Sensation functions [10].
A remarkable finding is that there are neural
If Φ (0) ≈ Φ (0), then the perceived direction of a activities at the inferior colliculus corresponding to the
ll rr
noise source in the horizontal plane is assumed to be IACC and sound energies for sound signals that
described as arriving at the two-ear entrance [11]. Also, it is
discovered that the LL and the IACC are dominantly
associated with the right cerebral hemisphere, and the
temporal factors, ∆t1 and Tsub, the sound field in a room Table 2. function (IACF). Spatial sensations in relation to
are associated with the left [2]. factors extracted from the autocorrelation function (ACF)
and the interaural crosscorrelation
Subjective Diffuseness
Factors Spatial Sensations
The scale value of subjective diffuseness is assumed
ASW Subjective Image Horizontal Vertical
to be given by Equation (10). In order to obtain the
Diffuseness Shift Direction Direction
scale value of subjective diffuseness, paired-
comparison tests with bandpass Gaussian noise,
varying the horizontal angle of two symmetric ACF
τ X
reflections have been conducted. Listeners judged 1
φ X
which of two sound fields were perceived as more 1
τ X
diffuse, under the constant conditions of LL, τIACC, and e
WIACC [12]. The strong negative correlation between IACF Φll(0) - - X X x
the scale value and the IACC can be found in the Φrr(0) - - X X x
results with frequency bands between 250 Hz - 4 kHz. LL X X - - -
The scale value of subjective diffuseness may be well τIACC x x X X x
W X X X X x
formulated in terms of the 3/2 power of the IACC in a IACC
IACC X X X X x
manner similar to the subjective preference for the

sound field, i.e.,
X: Major factors influencing the corresponding response.
S ≈ - α(IACC)β (12) LL = 10 log [Φ(0)/Φ(0)fref], where Φ(0) = [Φll(0) Φrr(0)]1/2;
diffuseness ASW: Apparent source width.

where coefficients α ≈ 2.9 and β ≈ 3/2.

REFERENCES
Apparent Source Width (ASW)

1. Y. Ando 1985 Concert hall acoustics, Springer-Verlag,
It is considered that the scale value of apparent
Heidelberg.
source width (ASW) is given by equation (10) as well.
2. Y. Ando 1998 Architectural acoustics, blending sound
For a sound field with a predominately low frequency
sources, sound fields, and listeners. AIP Press/Springer-
range, the long-term IACF has no sharp peaks for the Verlag, New York.
delay range of | τ | < 1 ms, and W becomes wider. 3. K. Mouri, K. Akiyama and Y. Ando, J. Sound Vib., 241,
IACC
Clearly, the ASW may be well described by factors, 87-95 (2001).
IACC and W [7], under the conditions of a constant 4. S. Sato, T. Kitamura, H. Sakai and Y. Ando, J. Sound
IACC
LL and τ = 0. The scale values of ASW were Vib., 241, 97-103 (2001).
IACC 5. E. Zwicker, G. Flottorp, and S.S. Stevens, J. Acoust. Soc.
obtained by paired-comparison tests with ten subjects.
Am., 29, 548-557 (1957).
The listening level affects ASW [13], therefore, the
6. M. Inoue, Y. Ando and T. Taguti, J. Sound Vib., 241,
total sound pressure levels at the ear canal entrances of 105-116 (2001).
sound fields were kept constant at a peak of 75 dBA. 7. Y. Ando, H. Sakai and S. Sato, J. Sound Vib., 232, 101-
Listeners judged which of two sound sources they 127 (2000).
perceived to be wider. The results of the analysis of 8. K. Saifuddin, H. Sakai, and Y. Ando, J. Sound Vib., 241,
variance for the scale values s indicates that both of 117-127 (2001).
ASW
factors IACC and W are significant (p < 0.01), and 9. P. Damaske and Y, Ando, Acustica, 27, 232-238 (1972).
IACC 10. S. Sato, V. Mellert and Y. Ando, Sound Vib., 241, 53-
contribute to the s independently, thus
ASW 56 (2001).
11. Y. Ando, K. Yamamoto, H. Nagamastu and S.H. Kang,
sASW ≈ a(IACC)3/2 + b(WIACC)1/2 (13) Acoust. Letters, 15, 57-64 (1991).
12. Y. Ando and Y. Kurihara, J. Acoust, Soc. Am., 80, 833-
where coefficients a ≈ -1.64 and b ≈ 2.44. Table 2 836 (1982).
indicates a list of spatial sensations with their 13. M.V. Keet, Proc. 6th Intern. Congr. Acoust., Tokyo,
significant factors extracted from the IACF. Paper E-2-4 (1968).
Fundamental subjective responses for the sound
field in a concert hall may be described by all of
significant orthogonal factors. For example, the scale
value of subjective preference is well described by four
orthogonal factors, i.e., LL, IACC, ∆t and T [1,2].
1 sub
The Preferred Acoustic Parameters
for a Javanese Gamelan Performance Hall
J. Sarwonoa,b and Y.W. Lama
aSchool of Acoustics and Electronic Engineering, University of Salford, Brindley Building,
Meadow Road Site, Salford M7 9NU, UK. E-mail: [email protected]
bEngineering Physics Department, ITB, Jl. Ganesa 10 Bandung 40132, Indonesia.
This paper discusses the application of a method based on human subjective preference to the acoustic design of a Javanese
gamelan performance hall. Some important distinctions between Javanese gamelan ensembles and Western classical orchestra
are the tuning system, orchestral blending process, and technique of playing. The results of subjective preference test using the
rank order method showed that the subjects preferred 30 ms for ITDG, 600 ms for RT, and the smallest value of IACC. These
results, except for the IACC, agree with the acoustic parameters from the room responses measured in a traditional pendopo in
Indonesia, which is not a common concert hall but an open-sided hall.
INTRODUCTION echoic chamber. A computer-based analysis has been
used to obtain the most appropriate gendhing for the
Javanese gamelan is one of the Indonesian whole subjective preference test, while computer
traditional music ensembles. There are several simulation process was mainly used for preparing the
important differences between the gamelan and the test samples. In situ measurements were conducted in a
Western symphony orchestra including tuning systems, pendopo in Indonesia to provide comparison for the
orchestral blending systems, and playing technique. subjective preference tests.
According to Ando[1], by using human preference All the subjective preference tests were carried out
approach through a psychoacoustic test, four in an anechoic chamber, using a configuration of 7
orthogonal factors for designing concert hall can be loudspeakers to simulate several sound field conditions
determined. Those four factors are the listening level to be judged by listeners. All the listeners were
(LL), the initial time delay gap (ITDG), the subsequent university students with several nationalities, inclusive
reverberation time (RT), and the Inter-Aural Cross- all genders. The subjective preference test has been
Correlation (IACC). So far, this theory has mostly been carried out using the rank order method.
applied for designing concert halls for Western A studio recording gendhing from the closing part of
classical music. Kebogiro Glendeng, with minimum τe = 27.59 ms (2T
This paper will discuss an application of the = 2 s, interval 100 ms), was used in the subjective
approach to design the preferred acoustic conditions preference test. The duration of the stimulus was 9.3 s.
for performing Javanese gamelan in an enclosed hall. All stimuli were stored in a PC, which was also
Three preferred parameters, ITDG, RT and IACC will functioned as stimuli player. Seven identical
be discussed in this paper. Measurement data from a loudspeakers were used to produce the sound. All
pendopo, an open-sided hall where Javanese gamelan loudspeakers were placed at distance of 1.35 m from
usually played, in Indonesia will be provided as the listener. The horizontal angles of the loudspeakers
comparison. were 0o, ±45o, ±67.5o, and ±135o. The vertical angles
of the loudspeakers were 0o, except the rear
METHOD AND EXPERIMENT SETUP loudspeakers for the ITDG and RT tests, which were
elevated 6o, relative to the subject's ears. The detail
configuration is shown in Table 1.
The research combines three major methods, a
computer based analysis and simulation, in situ
measurements and subjective preference test in an-
Table 1. Detail of Test Configuration
Direct Refl. Reverb. Refl. Reverb. Stimuli Listening Subject
Test Sound Amplitude Amplitude Level
ITDG 0o ±45o ±67.5o ±135o 1 dB -3 dB 15, 30, 50, 80, 160 ms 73 dBA 6
,
RT 0o ±45o ±67.5o, ±135o -1 dB 2 dB 0, 0.45, 0.6, 1.2, 2.5, 4.5 s 73 dBA 17
IACC 0o ±45o ±67.5o ±135o vary vary 0.3, 0.4, 0.5, 0.75, 1 73 dBA 10
,
RESULTS AND DISCUSSION
1000 35
It was shown that there was a low value preference 900
30
for ITDG (Figure 1) as well as for RT (Figure 2), with 800
the most preferred value of 30 ms and 600 ms, 700 25
rgtoeos oJpadev ccatlinaveresilteyy . g waTmihtheis l aannm eiinna tniamsn atethneac tsl ootsuhenedd shfuaieblllj.de Tcfthos er spleri sertfeeesnrurineltdgs RT (ms) 456000000 1250 ITDG (s)
agree with the ITDG and RT of pendopo Puro 300 10
Mangkunegaran[2], as shown in Figure 3. It shows the 200
5
100
0 0
5 centre 10 11 15 king
Measurement points
4 RT ITDG
r
e
d 3 FIGURE 3. ITDG and RT of Pendopo
Or Mangkunegaran
k
n 2
a
R
5
1
4
0
15 30 50 80 160 er
ITDG (ms) rd 3
O
FIGURE 1. Preference for ITDG k
n 2
a
R
6 1
5 0
0.3 0.4 0.5 0.75 1
er IACC
d 4
r
O
k FIGURE 4. Preference for IACC
an 3
R
2 CONCLUSION
1 The preferred parameters for Javanese gamelan
0 450 600 1200 2500 4500
performance hall were 30 ms for ITDG, 600 ms for
RT (ms)
RT, and the smallest value of IACC. These agree with
FIGURE 2. Preference for RT the acoustic parameters, except for the IACC, from the
room responses measured in a traditional pendopo in
Indonesia, which is not a common concert hall but an
ITDG and RT of the pendopo at 5 measurement points,
open-sided hall.
including the centre of the hall (centre), the audience
area (10, 11, 15), and the VIP area (king).
REFERENCES
Figure 4 shows that the lower the IACC the higher
the subjective preference. This shows that a
1. Ando Y, "Architectural Acoustics", Springer Verlag,
spaciousness and enveloping sound field is preferred
New York, 1998.
for listening to Javanese gamelan in an enclosed hall.
2. Sarwono, J. and Lam, Y.W., "The Acoustics of a
However, this is not in agreement with the measured
Pendopo: A Typical Open-sided Hall for Javanese
IACC of pendopo Puro Mangkunegaran, (IACC = 1) Gamelan Music Performance", in proceeding of IoA,
as it is an open-sided hall. 2000, Volume 22 Pt 2, pp. 305 - 313.
The Application of Neural Network Analysis to Auditorium
Acoustics
F. Fricke
Department of Architectural and Design Science, University of Sydney, NSW 2006, Australia.
[email protected]
Neural network analysis (NNA) is a relatively new research and design tool that has been used in many fields from structural
engineering to finance. So far very little use of the technique has been made in architectural acoustics. In this paper the NNA
technique is outlined and examples of its use in auditorium acoustics are given to demonstrate its potential. These include the
prediction of reverberation time and sound levels in auditoria and the acoustic quality of halls using both acoustic and physical
parameters as inputs. The advantages and limitations of neural network analysis are also outlined.
INTRODUCTION application to a number of architectural acoustics
issues has been described in a several papers by
Fricke eg [5],[6] and Nannariello eg [7],[8].
There are at least two approaches to the study of
The method is based on the way the brain works
concert hall and auditorium acoustics. One is academic
where neurons are connected by synapses. In a
and the other design oriented. The academic approach is
simple NNA the inputs (eg length and height of a
directed at finding out what it is that makes concert halls
room) neurons are interconnected to a layer of
good and what influences opinions about the acoustics of
“hidden” neurons which in turn are connected to an
halls. It is also about measuring and calculating various
output (eg the reverberation time or “acoustic
acoustic quantities in halls and trying to apply results of
quality” of a room) neuron. The network is trained,
perception experiments, carried out in anechoic rooms,
using data (cases) from existing situation where the
to more complex situations such as that which exist in
inputs and outputs are known. The error between the
concert halls. In the second approach the architect or
actual and predicted values of the output is
designer wishes to define the acoustics of a space in
minimised by systematically changing the weights on
terms of its size, shape and surface finishes.
the connections between the neurons.
While the approaches of Beranek [1], Ando [2] and
The advantages over other approaches are that
others shows great understanding of the academic
NNA can handle more than 6 input variables (usually
requirements these approaches do not give designers the
considered the maximum possible number for a
tools they want. These tools are simple rules of thumb
conventional analytical approach) and can deal with
that ensure excellent acoustics. Such simple rules almost
non-linear relationships. Its disadvantages are that it
certainly do not exist but more complex ones possibly
is never possible to determine whether an optimal
do. For instance, the most basic rule of thumb used
solution has been found and when a solution has been
seems to be the volume per seat even though the volume
found it cannot easily be used in the form of an
per seat varies between good halls (Boston Symphony
equation though it can be easily used in a spreadsheet
Hall has a V/N of 7.14 while Meyerson Hall has 11.6.).
format. Often there are not enough cases available to
A more complex rule may, for example, involve the
accurately train, verify and test a network and the
optimum volume per seat as a function of the length of
validity of the analysis is only within the range of the
the hall. Ultimately the aim of the present work is to
input variables. Also, where there are more than 6
investigate whether such complex rules exist and if so, to
inputs, it is very difficult to represent the output
present them in a designer-friendly form.
graphically or to produce rules of thumb from the
analysis.
NEURAL NETWORK ANALYSIS
NNA OF THE ACOUSTIC QUALITY
Very briefly, neural network analysis (NNA) is a
OF ROOMS
computer-based technique which learns to recognize
patterns. These patterns are usually in numerical data but
could be in the juxtaposition of pixels or the pitch of Of the two approaches tried for the prediction of
notes. The general technique and its applications have acoustic quality of rooms the “acoustic input”
been described in many texts eg [3],[4] and its approach [6] gives better results (Standard Deviation
Ratio, SDR ≈ 0.2) than the “geometrical input” approach are to be predicted falls within range of the training
[5] (SDR ≈ 0.9). This is not surprising given the large data for the neural network.
number of geometrical inputs required to define an There are limitations on the method and if NNA is
auditorium (though many of them are related to one to be a success there is a need for a data base on the
another). The geometrical approach required 10 inputs web where information can be made available to
(V, S, N, L, W, H, SDI, MRA, SH and SE) while the everyone. This is necessary as it is doubtful if any
acoustic approach required only 6 (5 of Beranek’s input one person is ever going to be able to undertake all
parameters – EDT, G, IACC, T, BR and SDI - and the measurements needed on halls in order to carry
I
either N or V) where V = room volume, S = room out satisfactory neural network analyses.
surface area, N = number of seats, L, W and H are the As a final comment it must be stated that NNA
maximum length, width and height respectively, SDI = should not be considered as a new branch of
surface diffusivity index, MRA = mean rake angle of architectural acoustics but rather as a new fertiliser
seating, SH is stage height and SE = stage enclosure. which may help the existing branches bear more
One modified geometrical approach which has given fruit.
useful results involves categorising halls into two
groups; those with an AQI of 0.7 or greater and those REFERENCES
with an AQI of less than 0.7. With this approach there is
> 90% success rate using N, L/W, H/V1/3, MRA and
1. Beranek, L. L., Concert Halls and Opera
SDI/SE as inputs.
Houses, Acoustical Society of America, Woodbury,
Another approach is based on Nannariello’s work [8]
NY, 1996
in which acoustical parameters, such as IACC and RT,
2. Ando, Y., Concert Hall Acoustics, Springer-
are obtained from geometrical inputs. These can then be
Verlag, Berlin, 1985.
used to calculate AQI. The efficacy of this method
3 Fausett, L., Fundamentals of Neural Networks:
should not be in doubt given Nannariello’s results for G,
Architecture, Algorithms & Applications, Prentice
RT, and IACC, (and the certainty that room acoustic
Hall, New Jersey, USA, 1994.
parameters are dependent on size, shape and surface
3. Statistica Neural Networks, (1999) Technical
finishes of rooms), but the final analysis has yet to be
Manual Version 4, StatSoft Inc., Tulsa, OK.
carried out.
4. Fricke, F. R. & Han, Y. H., (1999), A Neural
Network Analysis of Concert Hall Acoustics,
DISCUSSION AND CONCLUSIONS
Acustica, 85, 113- 120.
5. Fricke, F. R., (2000), Concert Hall Acoustic
NNA can be used to predict the acoustic quality of a Design: An Alternative Approach, Building
concert hall or an auditorium though the accuracy of the Acoustics, 7, 233-246.
“geometrical” approach leaves something to be desired. 6. Nannariello, J. & Fricke, F. R., (1999), The
Both the “geometrical” and “acoustical” NNA Prediction of Reverberation Time Using Neural
approaches are useful in understanding the influences on Network Analysis, Applied Acoustics, 58 (3), 305-
the acoustic quality of auditoria and giving an estimate 325.
of acoustic quality early in the design process. It appears 7. Nannariello, J. & Fricke, F. R., (2001),
likely that much better predictions of acoustic quality, Introduction to neural network analysis and its
using geometrical inputs and more complex networks, application to building services engineering,
will be developed soon. Once such a network has been Building Services Engineering Research &
developed and the network embedded in a spreadsheet Technology Journal, 22, 61-71
for designers to use. 8 Nannariello, J. & Fricke, F. R., (2001), The
Likewise, NNA can be used to predict acoustical Prediction of Reverberation Time Using suitable
quantities in auditoria such as RT (or EDT), IACC, G, Neural Networks, Proceedings 17 ICA, Rome
BR and T provided that the space in which the quantities
I
Objective evaluations of chamber music halls in Europe and Japan.
Takayuki Hidaka*, Noriko Nishihara*
* Takenaka R&D Institute, 1-5-1, Otsuka, Inzai, Chiba 270-1395, Japan
Abstract: The room acoustical parameters - Reverberation time RT, early decay time EDT, clarity C , strength G, initial time
80
delay gap ITDG, and interaural cross-correlation coefficient IACC , were measured in 18 major chamber music halls in Europe
E
and Japan employing the procedure in accordance with ISO 3382 [1]. By combining architectural data, the intrinsic parameters
for the acoustics of chamber music halls are examined.
INTRODUCTION volumes of the latter are about 40% larger. The reason
for the size differences appears to come from the fact
For symphony halls and opera houses, the results of that modern architects prefer medium-upholstered
measurements of current room acoustical parameters chairs for greater comfort. Because such chairs
have been reported in the literature [2,3]. There are absorb more sound, even when occupied, the room
only limited numbers of similar studies on chamber volume is larger in modern halls so as to adjust the RT
music halls [4]. There is no assurance whether to the volumes shown. The approximate equation
existing data or design guidelines for large symphony with the form, RT =K⋅V /S , is plotted in Fig.
M,occ A
halls are also suitable for smaller sized spaces,
1, where relevant K value falls between 0.13 and 0.14
therefore it seems meaningful to assemble the
for chamber halls, similar to the value of 0.14 for
acoustical data and to survey their features. In this
symphony halls [2], and RT’s seem to converge to ca.
paper, 9 highly-reputed halls of traditional design in
1.8 s.
Europe and 9 major halls of contemporary design in
C , EDT : C and EDT are variables not independent
Japan are compared and studied. 80 80
from RT, but all these are very highly correlated.
However, the subjective impression of clarity in
MEASUREMENT RESULT AND SOME
chamber halls is frequently of major concern. As
DISCUSSION
shown in Fig. 2, C ’s (occupied) may be classified
80
into two groups, (3.5±0.4) and (0.1±1.6) dB. The
The measured halls, which are regularly used for
latter coincides with the optimal range for Mozart
chamber music in each city, are listed in Table 1.
music which was proposed by Reichardt et al. [7].
European and Japanese halls respectively can be
Obviously every hall exceeds the lower limit of -1.5
classified as those of traditional style and those of
dB.
modern construction and materials. The seating
G : Strengths G in dB for traditional and modern halls
numbers, N, in these 18 halls vary from 207 to 844,
are moderately different from each other, except for
while the volumes, V, and reverberation times
hall SG, with the largest capacity N=844 (Fig. 3). G
(occupied) vary from 1070 to 8475 m3 and 0.9 to 2.0 s, L
and G of the former are respectively about 4.5 and 3
respectively. Many of them (15 out of 18 halls) are M
dB larger than the latter on average, which is probably
shoebox, or at least have rectangle floor plans. The
caused by the difference in volume, e.g., Beranek has
suffix “L”, “M” and “3” associated with the measured
shown G is proportional to 10log(EDT/V) [2].
quantities mean the average over 125/250 Hz, 500/1k
BR : The bass ratios for occupied condition are
Hz, and 500/1k/2k Hz, respectively. The occupied
distributed from 0.87 to 1.12 and from 1.07 to 1.24 for
values were transformed from measured unoccupied
modern and traditional halls (median values are 1.02
values using the method in [5].
and 1.14), respectively, which are narrower ranges than
The measurements were executed without audiences
that of the concert halls, 0.92 to 1.45 in spite of the
and with no instruments on the stage (sometimes a
wider range of V/N. BR highly correlates with G
piano existed at the corner of the stage). The L
(r=0.8), although Bradley and Soulodre find that G is
measuring procedure is exactly the same as in [3,5] and L
more significant [8].
coincides with that of ISO 3382 [1]. The correlation
[1-IACC ] : [1-IACC ] is also an independent
matrix for the objective measures shown in Table 2 E,80 E
variable for chamber halls but the variation range is
indicate that the independent parameters are RT , G,
M extremely narrow, 0.67 to 0.77. This range is same as
IACC , BR, and ITDG. This same correlation matrix
E3 the subjective difference limen by [9], namely it can be
is also found in symphony halls and opera houses [3,6].
said that every chamber hall has similar binaural
quality, provided [1-IACC ] is still valid for chamber
RT : The volume per person on average is 6.4 m3 for E,80
hall. This situation is quite different from that for a
traditional halls and 9.1 m3 for modern halls, thus the