Design and simulation of a benchmark room for room acoustic auralizations
Fotis Georgiou(1) , Baltazar Briere de la Hosseraye(1) , Maarten Hornikx(1) , Philip Robinson(2)(1) Eindhoven University of Technology, The Netherlands, f.georgiou@tue.nl(2) Facebook&Oculus, U.S.A., philrob22@fb.com

Abstract
In order to achieve accurate acoustic simulations of a room for obtaining an authentic auralization, the following
aspects need to be quantified: the geometrical details of the room, all material properties and the characteristicsof the source and the receiver. This paper presents the design of a benchmark room for this purpose, including
all this information. The room is a building acoustics transmission chamber with thick concrete walls. This room
was then acoustically treated to achieve an acoustic environment close to a day-to-day office room. The surface
impedances of the materials additionally installed in the room were measured with both in the impedance tube as wellas with a pressure-velocity sensor. Furthermore, the directivity of the measurement source and the binaural receiver
were measured in order to be included in the simulations. Impulse responses of this benchmark room have also
been obtained from simulations with the in-house time-domain discontinuous Galerkin method (DG) with frequencydependent boundary conditions, including source and receiver directivity. Time and frequency domain results fromthe both the measurements and simulations are presented, showing a close agreement.
Keywords: Auralization, Room acoustics, Acoustic modeling, Discontinuous-Galerkin

1

INTRODUCTION

Auralization can be a valuable tool for architectural acoustics as it provides more information regarding theacoustics of an indoor space compared to standardized room acoustic parameters such as RT60, C80 and STI(1, 2). With auralization, all the sound sources present in the environment can be simulated, and it can be used
to characterize the space with psychoacoustic metrics. Also, auralization can be a great communication tool (2).
Even though the concepts of auralization and acoustic modeling have been introduced over 6 decades ago(3), there are still many open questions. One of the key unanswered questions is how close the results ofa computer simulation can get to a real-life acoustic environment. This question does not only relate to theacoustic modeling method itself but also to the input parameters in the model, such as the acoustic propertiesof the materials within the modeled space and the characteristics of source and receiver.
Room acoustic modeling has been achieved with various methods. These methods can be split into three
categories: geometrical acoustics methods, energy-based methods, and wave-based methods.
Wave-based methods are techniques used to solve the governing partial differential equation(s) with numericalapproximations. Wave-based methods are physically accurate and inherently include complex wave-phenomena
such as diffraction (also modeled with high accuracy) (4). This is the main advantage of the wave-based methods compared to the geometrical acoustics methods. Among the wave-based methods are FDTD (5), the boundary element method (BEM) (6), the finite element method (FEM) (7), the digital waveguide mesh (DWM) (8),the finite-volume method (9) and the discontinuous Galerkin (DG) method (10). The main disadvantage ofthese methods is that they are computationally expensive. When the high frequency limit of the simulation isincreased, the discretization step in space (and time) needs to be decreased, which results in an increase of thecomputational load. Therefore, they become impractical at high frequencies (11).
In order evaluate the quality of a room acoustic simulation model all the geometrical details of the room,
furniture, material properties as well as the source and receiver characteristics need to be known in order toincorporate them inside the modeling method and compare the results against the measurements of that room.
This is very difficult to do in an existing room because of the following reasons: 1) it is difficult to quantify the
acoustics properties of the materials in-situ, 2) the room might include materials that are very difficult to include
in the acoustic simulations. Therefore, the design a of benchmark room, with a relatively simple yet realistic723

geometry that will be treated with acoustic materials that can be accurately quantified, is of high importance.
There are a number of papers that focused on the design and measurement of a reference/benchmark room
together with material measurements in order to evaluate different modeling approaches by comparing them tothe reference measurements such as (12, 13) and some of them also in the context of auralization (14). Moreover, some interesting review papers on this subject have appeared (1, 4, 15). The main outcomes are: 1) the
input material data is very important, 2) the geometrical methods fail to produce IRs equivalent to the measured
ones especially at low frequencies because they cannot model diffraction and modal interferences, 3) the low
frequencies can be modeled accurately using a wave-based method but due the fact that they become computationally expensive at high frequencies they can only be used for a limited frequency range. Hybrid approaches
that use wave-based method for low frequencies and geometrical acoustics method for high frequencies havebeen developed but there is no standardized methodology yet on how to do this (11). Also, there are still open
questions such as where the cross-fade frequency between the wave-based and the geometrical acoustics methodshould be.
The main objective of the research presented in this paper is to obtain measurements from a benchmark
room for auralization purposes and compare them with results from a high-frequency wave-based simulation
method. In the work presented in this paper the wave-based method is applied to a higher frequency range (2.5kHz) than in previous works. Moreover, in this research a benchmark room was designed and all the material
properties were measured both in the impedance tube and with the pressure-velocity (PU) sensor. In previous
works (12–14) reserachers made use of real-rooms, thus, they could not extract the acoustics properties from all
the room’s materials.
The reason that a wave-based method was used for the acoustic modeling is because such an accurate method
may answer how close the results of a computer simulation can get to a real-life acoustic environment. Therefore, it was decided to run a DG simulation up to approximately 2.5 kHz and compare the results against themeasured IRs. DG is used because it is able to handle complex geometries by discretizing the room volume
by non-overlapping mesh elements, and it allows to model time-domain frequency dependent boundary conditions (16).
This paper starts by presenting the design of the benchmark room. Next, the impulse responses (IR) and thebinaural room impulse responses (BRIRs) measurements inside that room are demonstrated. Finally, the resultsfrom the DG simulations are presented against the measurements.

2

DESIGN OF THE BENCHMARK ROOM

The empty building acoustics transmission chamber in the Echo building at Eindhoven University of Technology,
which has a geometry that can easily be meshed for a solution by the DG method, was chosen to be modified
as the benchmark room. The reverberation time of the empty building acoustics transmission chamber is very
high, ranging from 7.6 s down to 2.7 s between 125 Hz - 4000 Hz. At even lower frequencies, the reverberation
time is exceeding the 10 s. The reverberation time is rather homogeneous for different source-receiver (S-R)
configurations withing the room from the 1000 Hz band and up, but is not at lower octave-bands due to the
influence of the room modes at low frequencies.
This empty room was acoustically treated to achieve two room acoustic scenarios. The goal of Scenario 1
was to achieve the acoustics of a typical office room, with a reverberation time in the range of 0.5 s to 0.8 s
over the 125 - 4000 Hz octave-bands. Scenario 2 was designed as an office room with "bad acoustics". While
the reverberation time of Scenario 2 should not be as extreme as the one of the empty reference room, it was
designed to be significantly longer (in the range of 1.0 s to 1.3 s over the 125 - 4000 Hz octave-bands) to
reduce the speech intelligibility within the room.
The furniture and the materials that were placed inside the empty room had geometries that could be easily
incorporated in the DG mesh. Two type of absorbing materials were chosen for the acoustic treatment of theroom: porous materials for the wall panels and carpet tiles to cover the ground surface (see (17) for more
information regarding the material properties). These materials were chosen because they act as locally reacting
surfaces, which is important for their accurate modeling in the numerical simulations in DG. The furniture was724

placed to create some scattering in order to break down some room modes.
The geometry of Scenario 1 is shown in Figure 1. The uncertainty of the measured distances is estimatedto be smaller than 5 cm. The orientation of the objects (chairs, tables, lockers) was also marked on the floorusing gaffer tape. Finally, the locations and geometries of the objects were reconstructed in SketchUp models.
The origin of the coordinate system in the SketchUp models is the left bottom corner (see Figure 1). Scenario
2 was set-up by removing several elements which were present in the room for Scenario 1. The floor was left
with 11 m2 carpet tiles and the walls were left with 6 absorbing panels. The furniture in the room was also
reduced to 3 chairs and 2 tables. The ceiling remained untreated in both scenarios.

Figure 1. Top view of Scenario 1 including the source and receiver positions.

3IR, BRIR, SOURCE DIRECTIVITY, HRTF, AND MATERIAL PROPERTIES MEASUREMENTS

For each of the two room acoustic scenarios, measurements were performed with two different sound sources
using the B&K TYPE 4295 omnisource. The HATS was mounted on the turntable and its location was kept thesame at all measurements. The BRIRs were captured with a full rotation (360◦ ) in the horizontal plane with 5◦
angular resolution, and for 2 different source-receiver configurations with distances of 1.50 m and 3.50 m (seeFigure 1). The omnisource’s top opening was positioned at ear height facing the ceiling.
The measurements were carried out during evening and night time, in order to avoid noise disturbances fromthe nearby offices. The temperature in the room during the measurements was very stable (T = 21.6 ± 0.3 ◦C),and varied slightly from one day to another. The following measurement equipment was used: B&K 4128C HATS, B&K 1/2-inch free-field microphone 4189, Dell latitude E5530 laptop with Windows 10, Acoustics
Engineering-Amphion power amplifier and Acoustics Engineering-Triton +10 dB gain sound card. The measurement were conducted using the Acoustics Engineering-Dirac 6 acoustics measurement software. An exponential
sine-sweep of 175 s long was used as a measurement signal to achieve high signal-to-noise ratio.
The impulse-to-noise ratios (INRs)1 obtained from the measured BRIRs were within the range of 75 - 93
dB for the octave-bands 63 - 8000 Hz (see Figures 2), implying a very high quality of the measurements. An
initial analysis showed that the reverberation times at low frequencies (below 125 Hz) were very high. It was
not possible to reduce the low-frequency reverberation times below 1 - 1.5 s (see Table 1), which was one ofthe design goals. Attempts for further room treatment were made, which achieved a significant reverberation
time reduction at low frequencies. However, these treatments included objects that would be very difficult tomodel in the numerical simulations, as they act as non-locally reacting surfaces. Thus, it was decided not to
1 INR is equal to the maximum root-mean-square (RMS) level of the room impulse response (in dB) minus the noise level (in dB) (18).

725not add any extra objects in room other than the ones shown in Figure 1.

INR [dB]

100
80
60
40
20

102

103

104

Octave bands [Hz]Figure 2. INR of right ear BRIRs Scenario 1 measured at 0◦ for S-R distance 1.5 m (triangles) and 3.5 m(circles).
The HRTFs of the B&K 4128-C HATS and the directivity of the B&K TYPE 4295 omnisource where measured inside a semi-ancechoic chamber. The surface impedances of the carpet tiles and the sound absorbing
panels where measured both with the impedance tube and the pressure-velocity (PU) probe. More details regarding the impedance measurements can be found in (17).

4DG SIMULATIONS AND COMPARISON AGAINST THE MEASUREMENTS

4.1 DG simulations
The measured IRs and BRIRs were computed with the in-house DG MATLAB code, which is described in detail in (10). Because of the large unevenness of the reverberation times between the low and high octave-bands(see Figure 1), it was decided to run two sets of computations, a 12 s long calculation for the low frequency
range ("LF", up to 500 Hz) and a shorter one of 0.6 s long for the more computationally expensive mid-to-highfrequency range ("HF", from 500 Hz to 2.5 kHz) respectively and then combine them (see Subsection 4.4).
The meshes of the various scenarios were created from the Sketchup models of the geometry via the software
Gmsh. For the two sets of simulations, the characteristic length Lc , was set to the half of the wavelength
corresponding to the maximum simulated frequency fmax (Lc = c/(2 fmax ), where c is the speed of sound). For
the LF simulations fmax was set at 500 Hz and for the HF at 2500 Hz. In order to ensure accurate results up
to these frequencies, a polynomial order N = 4 was used in the DG simulations to obtain more than 10 degrees
of freedom per wavelength. Unfortunately, the post-analysis showed that the size of the elements of the DG
mesh for the HF simulations was not small enough to simulate the initial Gaussian distribution properly, whichgave directional characteristics in the initial condition. Therefore, the pressure from the initial condition was not
distributed equally in each direction causing directivity fluctuations <6 dB.
4.2 Boundary conditions
The frequency dependent time-domain boundary conditions (TDBCs) used in the simulation are the ones derivedin (17) for the carpet and the porous panel. They were applied to the relevant surfaces via the physical surfaces
definition tool in the Gmsh software. The hard walls of the room were assumed to behave as frequencyindependent reflective surfaces with a real reflection coefficient. This acoustic reflection factor was set to R =0.991, in accordance with a previous measurement of the empty room described in Section IV.D in (10), where
this reflection coefficient was derived from the quality factor of the strong modal resonance at 97.9 Hz.
4.3 Source and receivers
The source directivity of the measurement source was not incorporated in the simulations due to an issue
with the measured directivity data and was left for future work. The simulated source was an omnidirectional
Gaussian pulse, with a different source width for low and high frequency simulations. In order to reproduce theauralization binaurally via headphones allowing for head rotations, binaural responses need to be rendered fromthe DG simulations. This was implemented by storing the sound field over a dual spherical array of receiver726

locations, placed in the simulation domain. The receiver points consisted of two concentric spheres, an inner
sphere and and outer sphere, following the approach presented in (19). Next, spherical harmonics were applied
together with the database of measured HRIRs from the HATS used in the measurements, to post-process thedata to HRIRs for the same set of head rotations as used in the measurements.
4.4 IRs and BRIRs post-processing and combination of low and high frequency IRs and BRIRs
The calculations of the modeled IRs and BRIRs include the spectral characteristics of the initial condition,
which is a Gaussian distribution. A correction (equalization) filter was designed to correct for the Gaussian
spectrum based on the method presented in (20). Since two separate simulations were conducted, one for the
low and one for the high frequencies, two inverse filters had to be designed using the free-field time responsesof the low and high frequency Gaussian initial conditions.
The method developed in Section 4.3 in (11) was used to combine the low and high frequency IRs and
BRIRs. The only difference with (11) is that the combination was made in the time-domain and not in thefrequency-domain. The method is demonstrated in Figure 3. First, the cross-fade frequency between the low
and the high frequency simulations needs to be decided based on the limit of the low-frequency simulations. In
all the simulations presented in this report the cross-fade frequency was 500 Hz because the reverberation tails
from this octave-band and above were included in the high frequency simulations, which were much shorter induration compared to the low-frequency ones (see Section 4.1). Next, the low and the high frequency simulations were filtered with a 9th order zero-phase low-pass and high-pass filter respectively and the outputs are
summed together. The filters were designed using the butteworth.m function in Matlab and the zero-phase filtering was performed using the filtfilt.m function. The reason for performing zero-phase filtering and not normal
filtering is that the normal Butterworth filters cause a frequency-dependent delay and in order to avoid thiszero-phase filtering was applied. However, (11) states that the frequency-dependent delay does not add any audible artefacts in the IRs. Finally, a 8th order (normal) Butterworth low-pass filter was applied to the combined
output with a cut-off frequency that depends on the accuracy limit of the high-frequency simulations, which forthe simulations presented here is estimated to be around 2.1 kHz.
LF corrected IR or BRIR
resampled at 44.1 kHz

LF corrected IR or BRIR
resampled at 44.1 kHz

Cut-off = 500 Hz

Zero-phase Low-pass filter

Zero-phase High-pass filter

High-pass filtered HF IR

Low-pass filtered LF IR

Cut-off = 2100 Hz

Low-pass filter

Combined IR or BRIR

Figure 3. Flow chart of the combination of the low frequency (LF) and high frequency (HF) IRs.

727

Ampltiude [-]4.5 Measurement versus Simulations
In Figure 4 the measured IR of Scenario 1 for S-R distance 1.50 m is plotted against the simulated IR. The
matching at low frequencies is very good except from a big deep in the frequency response present in themeasured IR at approximately 53 Hz. At high frequencies the errors are larger because: 1) the issue with thelarge element size in the high frequency simulations, which is discussed in Section 4.1, 2) the directivity of
the B&K TYPE 4295 omnisource, which is not a perfect omnidirectional source above the 1 kHz octave-band,
was not included in the simulations. Regarding the IRs (time response), the matching is good but they are not
identical. The biggest difference is in the direct sound component.
In Table 1, the measured and simulated T20s of Scenario 1 and 2 are plotted. In Scenario 1 the matchingis very good at 500 Hz and at 1000 Hz octave-bands and in Scenario 2 the matching is very good between
250 - 1000 Hz octave-bands. At low frequencies the mismatch is very large in both scenarios. This mismatchis possibly caused by an inaccuracy of the TDBCs at low frequencies, especially below 100 Hz, where reliablemeasurements of the materials’ properties could not be performed. The reflective nature of the materials in thisfrequency range is potentially aggravating further the relative error in the measured absorption coefficients.
10 -4

5
0
-5

Relative SPL [dB]

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Time [s]
-50
-100
10 2

10 3

Frequency [Hz]Figure 4. Time (top) and frequency (bottom) response of the measured (grey) vs the simulated (black) IR ofroom Scenario 1 for S-R distance 1.50 m. Only the 0.5 s of the time responses are shown here, however, thefrequency responses are computed using the full 12 s of time signals.

Table 1. Measured vs simulated T20s in Scenario 1 (S1) and Scenario 2 (S2).
Octave-bands (Hz)Measured T20 in S1 (s)Simulated T20 in S1 (s)Measured T20 in S2 (s)Simulated T20 in S2 (s)

63
5.98
3.45
8.80
4.02

125
1.58
2.11
3.71
2.84

250
0.67
0.98
1.68
1.64

500
0.47
0.55
0.86
0.93

1000
0.47
0.51
0.76
0.73

2000
0.49
0.38
0.87
0.56

In Figure 5 the right ear simulated BRIR of room Scenario 2 at 0◦ for S-R distance 3.50 m is plottedagainst the measurement. The results show a similar degree of accuracy at high frequencies as the IR presented
in Figure 4. However, there is a bigger error at low frequencies because the HRTFs used in the sphericalharmonic approach to obtain the computed BRIRs are plane wave functions (see Subsection 4.3) and for nearby
sources this introduces some low frequency errors.

728

Ampltiude [-]

10 -4

5
0
-5

Relative SPL [dB]

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Time [s]

0
-50
-100

10 2

Frequency [Hz]10 3

Figure 5. Time (top) and frequency (bottom) response of the measured (grey) vs the simulated (black) right ear
BRIR of room scenario 2 for S-R distance 3.50 m and at 0◦ . Only the 0.5 s of the time responses are shown
here, however, the frequency responses are computed using the full 12 s of time signals.

5

CONCLUSIONS AND FUTURE WORKThis paper presented the measurements in a benchmark room designed for auralization purposes, and includes
results from the room acoustics simulations using the wave-based DG method. All details from the the measurement room were captured as input data for simulation methods. The results showed a good match between
the measurements and the simulations. However, refinement of both the input (proper source directivity data) as
simulations (improving the modelled source function in DG, using source directivity, using higher order spherical harmonics for post-processing the BRIRs) is foreseen to further improve the benchmark room dataset as wellas the wave-based results. Moreover, apart from the objective comparisons, subjective evaluations also need tobe conducted in order to investigate how much the measurements and the simulations differ from the perceptual
point of view.

ACKNOWLEDGEMENTS
This work was supported in part by funding from Facebook Technologies, LLC.

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