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
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