PROCEEDINGS of the
23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Progressive region-of-interest filtering for urban sound auralization
applications with multiple reflected and diffracted propagation paths
Jonas STIENEN; Michael VORLÄNDER
Institute of Technical Acoustics, RWTH Aachen University, Germany

ABSTRACT
When auralizing urban environments, two major challenges can be identified for the propagation simulation:
the extent of the built environment and the wave-based propagation features, above all diffraction into theshadow region. It is in the nature of open space that sound can propagate long distances. For the auralizationof city acoustics without a-priori knowledge on the sound source characteristics, huge data must be taken into
account. With the increase of freely accessible open format databases providing layers with simplified
geometric models of entire landscapes including buildings, a solution to create virtual acoustic environments
based on this data is desirable. In this attempt, a progressive broad-band propagation simulation model basedon Geometrical Acoustics is proposed that primarily exploits the frequency-independent energy loss subject
to geometrical spreading of spherical waves for the spatial filtering of the geometry input. An ellipsoidal hull
representing a region of interest is constructed from the source and receiver position as well as sound powerand sensitivity, respectively. Subsequently a fast filtering algorithm with linear complexity removes
approximately irrelevant geometry for each propagation step releasing the propagation algorithm fromexecuting ray-polygon intersection tests on the entire geometry dataset and speeding up the propagation
simulation significantly.
Keywords: Virtual Acoustics, Outdoor Sound Propagation, Geometrical Acoustics

1. INTRODUCTION
The auralization of urban environments is an aspired aspect for the forecast of our future city
soundscape. The increasing generation and integration of digital geometric data as well as the progress
to make federal procedures transparent lead to freely available online data, like the street and building
maps published and maintained by the cadastral office of North-Rhine Westphalia, Germany 1 .
Semantic data layers of various context, which enrich plain mesh files with surface material, traffic
statistics, visitor counts, etc. form a vital information basis that invite multidisciplinary research
parties to simulate current and future aspects of city life.
Obviously, the feasibility for the prediction of dynamics of sound is one of the questions raised. For
environmental noise assessment, it has been demonstrated that semantic geometrical data can be
integrated into acoustic prediction models, for example by Lu et al. (1) and Kumar et al. (2). Thosemodelling approaches integrate noise immission over a long-time span – usually hours – and present
results in perceptually weighted energetic frequency bands. Sound sources are commonly represented
by point sources and line sources producing a steady-state sound power spectrum. Judgements of thecompliance with legal directives of these well-established noise levels can only be justified byconsulting experts.
Auralization, in contrast, addresses the auditory perception and is intuitively comprehensible bylayman, hence may be an effective tool for public information campaigns. In this sense, theenvironment is not evaluated to generate exhaustive noise maps, but focus must be laid on sensitive
receiver points, ideally arbitrarily chosen by the user in three-dimensional space. Relevant objects inthe vicinity of the sound source and the receiver must be considered, hence especially the geometry of
the neighborhood region is of interest. A major difference between noise assessment and auralizationis the fact that the latter does not average acoustic values over time but produces time series of pressure ,an audio stream, that can be used for acoustic presentation of the scenario. Hereby, the time-variant
acoustic transmission of each sound source is simulated by superimposing propagation paths to the1

Geoportal.NRW, https://www.geoportal.nrw, accessed Mai 20191713

receiver via the built environment. Dynamics of the scenario are adapted and are processed by meansof digital signal processing (3). In theory, effects like distant highway noise immission appearing as asteady-state noise spectrum following the spreading loss of a line source is inherently produced byphysically modelling all single sound events. Ground effects and level corrections must not be
considered, as the processing of coherent sound from a sound source is applied. However, in a dynamic
acoustic environment the simulation of propagation paths and the representat ion of such paths in the
DSP network is demanding and requires the capability to consider higher order reflections anddiffractions in order to maintain an output that is free of perceivable artifacts from the time-variant
adaption (4).

2. AURALIZATION IN THE CONTEXT OF ENVIRONMENTAL NOISE
Auralization is the process of making sound audible by physical means and with a resolution
matching or exceeding the auditory processing capability. A distinct challenge is the high-quality
requirements laid upon the input data. A virtual acoustic scenario is recreating a situation where asingle user perceives sound propagated through the environment that is originating from one or manysound sources. In the context of environmental noise, this requires a precise spatial mesh of buildings
as the user can approach facades closely and move around corners where effects like sound shadowing
is prominent and even small movements can significantly alter the result. In general, the possibility torepresent buildings is of high resolution and surpasses the highest wavelength of interest by far, i.e.
structural details below ߣ ൌ ͹݉݉ can easily be represented, as also Kamrath et al. pointed out
fittingly (5). The available data from online databases can readily be included without further
refinement for auralization. Even level-of-detail concepts that only rudimentarily approximate
buildings (e.g. by coarse bounding hulls) are practical in acoustics, if they do not significantly diverge
from a visual model in an audio-visual Virtual Reality application.
In contrast to the spatial resolution, the temporal resolution of current noise assessment measures isrelatively low, as it integrates over time in a magnitude of hours. Input data from sound sources andresults from noise maps and suchlike will hardly be reasonable to integrate in auralization applications,mainly because the energetic values have averaged out all temporal details. The result of anauralization application delivers a pressure signal over time at a receiver location including all
temporal variations a human being is sensitive to. However, individual results implementing outdoor
sound propagation for environmental noise are of use. The individual character of a perceived sound,i.e. the emitted signal, is carrying information that is associated with naturalism, realism andauthenticity (6). Modification of this signal by filters approximating the propagation effects, even
without phase information and with low spectral resolution, maintains the typical sound and achieve
successful auralization results (3). Increasing spaciousness and presence is then achieved byappropriately adding the environmental influence, which is the prime target of auralization. Apartfrom the high demand on the input signal of sound sources, the determination of the transmission
within an urban scenario must be computed correctly in the sense that the filtering of the signal emitted
by the source reflects the expected effects caused by the surrounding, like reflections and diffractions.
Additionally, relative movements must be accounted for, namely the Doppler sh ift either caused bysource movement or by receiver movement, at least when relative distance changes result in aperceivable frequency shift (7). In the context of environmental noise, implementing this dynamic
component is one of the most challenging aspects and requires time-variant processing for eachindividual path.

3. PROGRESSIVE REGION-OF-INTEREST FILTERING
3.1 Breaking down the number of propagation paths
One of the greatest challenges of sound propagation simulation is the sheer size of the input data
representing the built environment of urban areas. Resources like the OpenGeoData NRW 2 provide
files including areal patches of ͳͲͲͲ m by ͳͲͲͲ m extend, each including several thousand buildings
in case of densely populated regions. On the one hand, auralization demands arbitrary positioning of
acoustic entities, on the other hand it is apparent that only very few structures in the vicinity of theseobjects actually play a role for the propagation of sound waves. Hence, a practical procedure to find21714

https://www.opengeodata.nrw.de, accessed May 2019and separate these structures is obviously of major interest. This requirement becomes even more
important for acoustic algorithms that tackle early reflection simulation problems because they usuallyscale exponentially with reflection/diffraction order to the base of polygon count. Therefore, a method
to filter a region of interest from the whole database is desirable, that automatically includes all
relevant objects (polygons) and efficiently dismisses irrelevant data. If the decision process islightweight yet driven by acoustic semantics and sorts out objects as soon as possible, the potentiallymore sophisticated subsequent simulations gain a large profit from adjusted input data without
irrelevant areas. ‘Irrelevant’ in this context means, that the large part of the acoustically perceivable
paths should still be detectable within the subset, while those being most likely undetectable by thehuman auditory processing should be culled. Such a perception-progressive filtering trait can’t be
avoided since theoretically the dynamic range of acoustic transmission is unlimited , for example,
endless paths can be found that bounce between parallel facades or circle around buildings. However,
it is clear that on the emission end, the sound power level of common sound sources appearing in urban
environments have an upper limit, and on the receiving end, a certain detection threshold can beassumed. For humans this limit may go as low as the threshold of hearing (9) and for technical devices
until a specified noise floor is reached. Both the upper and lower limit produce a dynamic range thatcan be exploited in an acoustic path culling method which can be adjusted to meet the application
requirements. For example, in Virtual Reality applications, these adjustments may be pushed to aminimum to accelerate simulation computation by cutting down input data, while for expert listening
tests another setup is required.

Figure 1: Culling ellipsoid for a source-receiver-pair in an urban environment.
3.2 Distance-dependent path culling
The fundamental concept of the presented approach is the assumption, that sound waves, if
examined separately along the wave front normal, are solely subject to attenuation of various kinds.
The most prominent influence can be seen in the spreading loss or geometrical diversion , but also
reflection, diffraction and air absorption play a role. After emitted by the source, amplitudes aremonotonously fading in the entire frequency range. Predominantly the impact of spherical spreading
loss can be regarded as the most natural aspect of spanning a spatial area of relevance around the soundsource. Fortunately, virtual acoustics commonly renders scenes where the assumption of a point source
can be made, and the total sound field at a receiving point is resulting by coherent superposition of all
individually determined propagation paths. Hence, if each path is separately investigated and a
distance-dependent culling algorithm is applied, regions where constructive interference of sound1715

waves occurs may well possibly lead to increased levels at greater radii from the source, e.g. whenreflections focus energy. However, when searching for propagation paths, the combination of source
and target positions pose the initial task: which of the surrounding mesh items must be integrated forthe path construction? Figure 1 shows an example of a simple virtual urban environment where a few
sound path candidates are depicted, that include a first order reflection or diffraction. The paths
indicate a detour over faces and edges that becomes larger the farther an object is located from the axis
between source and receiver. For far distances, the spreading loss itself will largely dampen thetransmitted sound and is likely to undergo perceptual thresholds, either dictated by the auditory system
as ultimate rational or by a context-dependent defined threshold that is adjusted by applicability. In
both cases, an estimate of the maximum sound power level of the source must be known a-priori. In
contrast, the definition of a fixed dynamic range reserved for acoustic transmission may be a feasible
configuration if the sound characteristics can be assumed to lie in a similar region. Once the remaining
dynamic range – or maximum distance - is exhausted, the propagation path search can be aborted. The
determination of the maximum allowed distance for the culling algorithm is derived from the ISO
formula of sound pressure level ‫ܮ‬௣ at receiver given a point source with sound power ‫ܮ‬௪ and the
distance-dependent spherical spreading loss‫ܮ‬௣ ሺ݀ሻ ൌ ‫ܮ‬௪ െ ʹͲ ଵ଴

݀െ ͳͳ
݀଴(1)with ݀଴ ൌ ͳ m. Converted to obtain the maximum distance for an adjustable perception threshold‫୲ܮ‬୦୰ୣୱ୦ , it follows݀୫ୟ୶ ൌ ݀଴ ‫Ͳͳ ڄ‬௅ೢ ି௅౪౞౨౛౩౞ ିଵଵଶ଴

(2)

To give a short example, a distance of about 3ͲͲ m would be found for a sound power of ͻͲ dB and athreshold level of ͵Ͳ dB without any distinct refinement of the precondition. For simplicity, nofrequency dependent solution is presented with the comprehension that lower frequencies are notdamped extensively over distance, except for the geometrical diversion term, though can be largely
perceived by a human. Because searching paths in geometrical acoustics is a broadband approach andonly gains from frequency-separate processing if a level-of-detail concept is applied, worst-caseconditions must be assumed. This limitation would lead to a benefit of frequency-dependentconsiderations only in rather unusual situations, for example, when a source with exceptionallypronounced high-frequency components is marked relevant, although the air attenuation duringtransmission would largely attenuate the source’s spectrum.
The region of interest reflected by the constraint of a maximum detour is constructed with triangles
represented by vertex points of the source, the receiver and the contact point at the boundary. In
three-dimensional space, the mathematical formulation results in a rotation -symmetric ellipsoidal hullthat practically exhibits focus points at source and receiver, which is merely a property inherited by therotation symmetry, but otherwise not found in ellipsoids. The reduction of the problem to a
two-dimensional setup is depicted in Figure 2 and reveals this relation.

Figure 2: Cross-section of the propagation constraint of maximum detour reduced to a 2D ellipse problem.

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A transform from the global into the local coordinate system of the ellipsoid results in thesimplified conditional term for an internal point test following‫ݔ‬ଶ ‫ݕ‬ଶ ൅ ‫ݖ‬ଶ
(3)
൅
൏ͳ
ܽଶ
ܾଶ
with ܽǡ ܾ the major and minor axes of the ellipse. For the moment, the transformation must be
applied to all vertex points of the building mesh under test. Transferred to a typical urban situation, avirtual hull for a given source-receiver pair is depicted in Figure 1 indicating an inner and outer region
separating buildings that are relevant from those considered irrelevant, respectively. Buildings lying
within the ellipsoidal hull are kept for the acoustic propagation path finding, the residual data isskipped. Performing such a filtering as a pre-processing step is able to simplify the geometrical input
data set largely, however is only increasing efficiency if the test is lightweight and reliable.
3.3 Geometry data filtering for a rotation-symmetric ellipsoidal hull
Testing the intersection of one object with N other objects exhibits a linear complexity, if theintersection computation is constant. Hence the decision process, whether a building is inside oroutside the rotation-symmetric hull, has linearly increasing runtime connected to the number ofbuildings, which is desirable. The faster the single intersection test can be computed, the better theoverall result. An efficient algorithm performs the filtering (or culling) of geometrical objects byapproximating the mesh items (faces, edges) of a building on convex hulls making an efficient
three-folded intersection test possible. In the context of urban sound auralization, an appropriate
solution is proposed by running a pre-test on the intersection of a spherical hull. It is constructed by
the center point located at the barycenter of the complex building geometry with the radius determinedby the farthest mesh point distance relative to the barycenter. The suggested approach requires
semantic layers connecting buildings with corresponding geometry. Additionally, a pre-processing
step generating the building hulls must be performed, which is moderate in computation time andremains constant for static objects. In case of data formats based on the CityGML convention, suchproperties are already largely at hand or can be simply extended, and even data from lower detail levels
can be used, for example, from shoeboxes of LoD-1, if feasible for the given virtual situation.
The culling algorithm can be depicted from the flow diagram in Figure 3 and show the culling
procedure that must be performed for every building. Firstly, the sphere hull of the building under test
is used and an intersection of the outer sphere that fully contains the ellipsoid is constructed. Any
object outside the outer sphere is considered irrelevant and can be skipped from further examination.

Figure 3: Culling algorithm flow diagram.

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The required intersection test breaks down to a conceivably simple distance measure, because two
spheres only intersect if the distance between the center points is smaller than the sum of the sphereradii. Another advantage is, that spheres are rotation-symmetric and hence a transformation of thebuilding mesh is unnecessary, which makes this test remarkably fast. Accordingly, an inner sphere isconstructed that lies entirely inside the ellipsoid. Any object within this inner sphere is automatically
relevant and must be included. A third test is performed on the residual data set that is not marked
irrelevant but still lies outside the inner sphere. In this indefinite case, a more so phisticated andcostlier test is performed that evaluates the actual mesh data by transforming the mesh points into theellipsoid reference frame. Equation (3) is then evaluated and the building is considered irrelevant, if
any of the mesh points lies inside the hull, otherwise it is included. This approach inherits anapproximation error that leaves objects undetected if the curvature if the ellipsoid is steep (relatively
short maximum distance) and the objects under test reveal relatively large flat facades, hence
precautions have to be carried out if such data may be expected. On real urban data it could beobserved, that only a few percentages of buildings fall into the indefinite data set and the possibility
that a falsely excluded object may have a perceivable effect, i.e. constructs a valid path that would
have been audible, remains highly improbable. This is supported by the fact that close -bysource-receiver pairs with a practical dynamic range for the integration of propagation paths based on
the proposed algorithm construct almost spherical ellipsoids without a steep curvature in the sense thatit may play a role for a failure of the test. A visualization of the culling result performed on a real data
set of an inner city is shown in Figure 4, where the circles represent a sound source and a receiver,respectively, and the buildings colored in orange are marked relevant buildings for a further
propagation simulation, while transparent buildings are omitted. The shape of an ellipse can be readfrom the image.

Figure 4: Visualization of the acoustics-driven geometry filtering algorithm applied to a real urban data set.
To emphasize the resulting ellipsoidal shape, an exaggeratedly loud sound source has been used.
3.4 Extending the filter algorithm for higher reflection and diffraction orders
The presented culling algorithm has been introduced for a single reflection or diffraction
component along the propagation path. Without restriction, this concept can be extended to higher
order reflections and diffractions (including any combination). For the straightforward determinationof geometrical propagation paths emitted from the source and rece ived by the sensor, interactions
follow Snell’s law for reflections and Fermat’s principle for diffractions. The challenge of increased
complexity concerning verification tests is extremely demanding, because they have to be carried out

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on the entire data set. Any attempt to restrict the relevant geometry accelerates the procedure
substantially. The proposed culling algorithm can be iteratively performed on the residual data set
order after order, as every additional interaction of a path with the geometry concludes in an additional
detour. Before a constructed propagation path is verified, for example, by an intersection test that
decides if the path is audible, an integrated culling detection following the proposed algorithm can be
performed that is able to sort out impossible paths efficiently. It can be expected that propagation paths
departing from the direct line-of-sight between source and receiver become increasingly improbableand can therefore be removed at a very early stage of the acoustic simulation.

4. EVALUATION
Runtime evaluations have been carried out on a standard desktop computer with an In tel® Core™
i7-7700 CPU and 8 GB DDR4 memory running a Microsoft® Windows 10 operating system. For the
proof of linear complexity ܱሺܰሻ and scalability based on the number of objects ܰ, a benchmark of
the culling algorithm was conducted including a) a constant dynamic range with increasing building
density and b) increasing dynamic range with constant number of buildings. For controlled conditions
concerning the variation of the built environment, a randomized 3D city model (10) was used, thatgenerates an equidistant raster of variable building shapes. Separate runtimes of the three individual
validation components that must be performed for each building in worst-case revealed that amaximum of about ͵͹Ͳ ns can be estimated (cf. Table 1).
Table 1 – Averaged runtimes of individual validation components.
Validation test

Runtime average

Outer sphere intersection

52 ns

Inner sphere intersection

53 ns

Ellipsoid intersection

265 ns

The first test varied the building number from 63 to ͳ͸ǡͲͲͲ with some exponentially increasing
intermediate points. An artificial building data set was generated that increases the equally distributed
density on a square area with edge length of ͳʹͺͲ m. Neither buildings nor source and receiver were
elevated. The receiver was located at 500 m/500 m and the sound source was located at 800 m/800 m
and the maximum allowed geometrical detour resulting from a constant dynamic range wasdetermined to ͸ͲͲ m. Figure 4 indicates a good agreement with a linear context.

Runtime [ms]

8
6
4
2
0
0

2000

4000

6000

8000

10000

12000

14000

16000

Number of buildings

Figure 5: Runtime for increasing number of buildings at constant culling distance.
The second test distinguishes from the first test primarily by keeping the spatial extend and number ofbuildings constant, while varying the propagation distance between ͶͷͲ m and ͻͲͲ m withintermediate values at ͷͲ m increment. Figure 5 shows only a moderate increase of runtime beginningat ʹ ms for the smallest distance and linearly increasing by roughly ͺ͵ ns per ͷͲ m increment.

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Runtime [ms]

3
2.5
2
1.5
1

450

500

550

600

650

700

750

800

850

900

Propagation distance [m]Figure 6: Runtime for increasing culling distance at constant number of buildings.
The resolution why the runtime changes over a constant building number lies in the different
calculation complexity of the individual test components. With increasing distance, the number ofbuildings that cannot be culled immediately by the spherical intersection tests rises with the size of theellipsoid. Hence, the relatively costly ellipsoid intersection test must be performed more often ,explaining the result. In summary, it can be said that the algorithm performs as anticipated.

5. CONCLUSIONSIt has been shown that filtering geometrical input data from huge data sets based on considerationsof acoustic transmission in urban environments and perceptual properties of the receiver can be
implemented efficiently and with an adjustable configuration (dynamic range). The progressive natureof culling potentially undetectable geometrical propagation paths by removing building geometries
outside a region of interest requires careful observation and experimental validation, if pushed to thelimit. Otherwise, the proposed algorithm can facilitate urban sound auralization of single soundsources by reducing significantly the geometrical input data and, in consequence, easing calculation
demands for subsequent acoustic simulations, which otherwise would lead to an impossible
calculation runtime.

ACKNOWLEDGEMENTS
The authors would like to thank the German Research Foundation (DFG - Deutsche
Forschungsgesellschaft) for funding the project Auralization of Urban Environments – Real-time
simulation of Diffraction under grant number VO 600/39-1, which made this contribution possible.

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