Real-Time Room Acoustics Planning

Sonke Pelzer, Michael VorlanderInstitute of Technical Acoustics, 52074 Aachen, Germany, Email: spe@akustik.rwth-aachen.de

Introduction

Over the last decades computer-based room acousticalsimulation has emerged to be a powerful tool in the ar-chitectural planning process. It enables to facilitate de-cisions on room shape, material selection and optionallyon the design of a sound reinforcement system. However,commercial room acoustics simulation software is mainlyused by a small group of experts, such as acoustic con-sultants and researchers. At the same time new acousticsalgorithms have evolved in the domain of Virtual Real-ity (VR), where it is desired to reproduce virtual soundevents as realistically as possible.

Those state-of-the-art real-time simulations alreadyachieve accurate processing, a high reproduction qual-ity and interactive update rates, so that they are highlyinteresting for application in classical domains (acous-tic consulting, research) and could also pave the way forarchitects to directly integrate acoustics into their workflow. Applying modern interactive room acoustics sim-ulation during the architectural planning process givesthe architect complete control on the acoustical conse-quences of the room design during the early modelingstage and throughout the whole design process, even ifonly a trial-and-error paradigm is applied. To integrateacoustics into the workflow of architects, it is necessaryto provide appropriate interfaces between room acousticssimulation and CAD modeling software.

This article describes the key components of room acous-tics simulations to achieve interactive update rates, fol-lowed by interfaces such as plug-ins for typical 3D CADroom modeling tools, a validation of the used simulationalgorithms and finally a selection of example applicationsopening new possibilities.

Challenges

Room acoustical simulations tools are developed sincethe late 1960s, when Krokstad et al. started using the raytracing technique to predict acoustical room responses[2]. Although the available computation power has sig-nificantly improved since then, ray tracing is still com-putationally too expensive to be performed in real-timewith high update rates. This holds true for both acous-tical and visual rendering techniques. However, becausefor the time- and latency-critical early part of an im-pulse response, much faster methods are available (suchas the image source method [3]), modern interactive sys-tems include acoustical ray tracing for the late part ofthe impulse response[8, 4].

The challenges of such interactive room acoustical sim-ulations are to harmonize physical accuracy, perceptual

demands and algorithmic complexity. An elaborate ana-lyze and control stage has to react appropriately on userinteraction. The computational cost incurred if a virtualscene is subject to modification may vary significantly,dependent on the type of modification. This is further il-lustrated in Figure 2, which indicates the necessary com-putations for a certain scene change. In Figure 1 a basicoverview is provided how the computational cost riseswhen elements of different type are subject to modifica-tion. One of the cheapest operations is to update thesound reproduction in case of a rotation of the receiver,a typical case when using head-tracking. On the otherside, the most expensive operation is a change to thegeometric structure of the virtual scene. It requires acomplete recalculation of the sound propagation and im-pulse responses, and prior to that a significant time torebuild acceleration structures for the simulation algo-rithms, which are outlined in the next section.

Receiver Orientation

Receiver Translation

Receiver Directivity

(e.g. HRTF)

Source Orientation

Source Translation

Source Directivity

Material Moving Occluders

Room Geometry

Figure 1: Challenges in interactive room acoustics simu-lation. Head-tracking (receiver orientation) can be imple-mented very efficiently, while changes to the geometrical roomstructure are very challenging in real-time.

Object Event Image Sources Ray-Tracing

Filter-synthesis

Build Translate Audibility

Receiver

Rotation ●

Translation ● (●) ●

Directivity ●

Source

Creation ● ● ● ●

Rotation ●

Translation ● ● (●) ●

Directivity ●

RoomMaterial ● ●

Geometry ● ● ● ●

Figure 2: Possible interactions with the virtual scene requiredifferent recalculations.

Acceleration

The most frequently used operation in geometrical roomsimulations are intersection tests between rays (repre-senting the sound propagation) and polygons of the scene

DAGA 2015 Nürnberg

1139

geometry. This operation is responsible for nearly the allthe calculation time, so that an acceleration can signif-icantly improve the overall performance. The introduc-tion of spatial data structures can decrease the computa-tion time of each single intersection. The speed-up is de-pendent on the complexity of the spatial data structure.Typically, a more complex data structure also offers highperformance gain. The following algorithm classes aresorted descending by complexity and performance:

• Binary Space Partitioning

• kd-Trees

• Octrees

• Bounding Volume Hierarchies

• Spatial Hashing

However, the low complexity of the latter algorithms,such as spatial hashing, has the advantage of much fasterrebuild times. Former approaches aimed at separatingstatic and dynamic geometry or separating between mod-ification and listening phases to apply different spatialstructures (binary space partitioning (BSP) and spatialhasing (SH)) at the same time [12]. Both ideas implicateda restriction in terms of full user interaction. Therefore, arecent suggestion was to use SH only for the early part ofthe impulse response (low reflection order) and BSP onlyfor the late part [4]. Using this hybrid method offers highupdate rates for the perceptually prominent early part,while using the highest performance for the ray tracingpart, which requires orders of magnitude more intersec-tion tests. An exemplary simulation activity is depictedin Figure 3. The Bounding Volume Hierarchies (BVH)can be seen as a compromise solution, offering fast re-build times, while still having competitive performance.

Figure 3: Simulation sequence after an interaction event,with a detected room geometry change. Direct sound andearly reflections are updated with very low latency using theflexible spatial hashing technique, while the costly ray trac-ing waits for the high-performance binary space partitioningstructure.

In ray tracing algorithms for computer graphics, the useof SIMD instructions provided further performance gains.Because these rely on consistent ray coherence, they arenot likely to perform well for acoustical ray tracing, wherescattering breaks up ray packets and typical reflectionorders are much higher. Lately, hybrid strategies thatused BSP and BVH have been proposed to overcome thisproblem [6]. However, to exploit the resources of modern

CPUs, it is necessary to execute code in parallel. Theray tracing technique is well suited for parallel execution,because of minimal data dependencies between threads,so that nearly perfect scalability to CPU cores or clusternodes have been reported [8, 9, 10].

Real-time room acoustics implementation

This section introduces a recent implementation of astate-of-the-art real-time room acoustics simulation. De-veloped at the Institute of Technical Acoustics at RWTHAachen University, it takes advantage of hybrid geo-metrical acoustics algorithms, hybrid spatial data struc-tures, and multi-layer parallelism (node-level, multi-core,SIMD) [8, 7, 9]. This enables the application for real-timesimulation, visualization, and auralization including low-latency convolution for 3-D reproduction via loudspeak-ers or headphones. Supposing that enough computationpower is available, large virtual scenes can be simulated,including effects such as directivities, doppler shifts, scat-tering, higher-order edge diffraction, and sound trans-mission through walls. The room geometry, materialdata, and acoustical source/receiver characteristics canbe modified at run-time through manifold interfaces thatare introduced in the next section. The reproductionmodule renders source signals by convolution with sim-ulated spatial 3-D impulse responses, with signal pro-cessing for binaural headphones, crosstalk-cancellation,multi-channel intensity panning and higher-order Am-bisonics. The spatial impulse responses can also be com-posed in a hybrid manner using multiple reproductiontechniques [11].

Interfaces

A room acoustical simulator is traditionally operated byusing a graphical user interface (GUI). However, for real-time application it is favorable to access the simulationcore directly using a network protocol, such as TCP/IP,or linking the library directly. This requires a dedicatedmaster application, for example an existing virtual realityframework [12].

Figure 4: Example code of a MATLAB script, showing areceiver position translation and a material property changewith simple commands. Finally the an auralization is pre-pared using convolution.

In research it is often desired to run scripted sequences,e.g. preparing many variations, analyzing influence of acertain parameter, preparing series of auralizations forlistening tests, or preparing animations. Therefore a di-rect link to a scientific programming language, such asPython or MATLAB, must be established. In Figure 4

DAGA 2015 Nürnberg

1140

an example code of a controller interface for MATLABis shown, illustrating how the receiver position or a ma-terial’s absorption can be changed easily with a final au-ralization using convolution.

Considering that a room simulation cannot be done with-out prior preparation of the 3D CAD room model, thegeometrical input is one of the crucial parts of the userinterface. If the geometry is about to be modified in-teractively, for example during an architect’s design pro-cess, this CAD modeling functionality must be well engi-neered and user-friendly. The fact that very comfortable3D modeling programs already exist and are frequentlyused suggests that integrating the acoustics simulatorinto such modern programs makes perfect sense. As aprove of concept, our real-time room acoustics rendererwas integrated into Trimble SketchUp [13, 4], as shownin Figure 6.

C++ Simulation Framework

Message-Processing

Thread Pools

RAVENSimulationSpatial Impulse Responses

Direct Sound

Early Reflections

Reverberation

SketchUp(3D-Modeling)

Ruby-Plugin(GUI, Material Editor, Simulation-Control)

TCP/IP-Network

Sources, Receivers, Geometry, Materials,Simulaton Configutaion and Control

Simulation Results, Audio-Devices, Messages, Control

Low-latencyConvolution

Input Audio Stream(File, Livestream)3D Audio Stream

Figure 5: Using a plug-in for the CAD modeler SketchUp, allrelevant scene information is transmitted over network to theroom acoustics simulation in real-time. The multi-threadedsimulation renders 3-D audio and returns results for visual-ization (e.g. room acoustics parameters) back to SketchUp.

The connection between SketchUp and the acoustics sim-ulator was achieved by enabling a network connection be-tween the two programs, as shown in Figure 5. A plug-infor SketchUp was developed in the programming languageRuby, implementing a TCP/IP server that transfers ge-ometry data including materials, as well as sound sources,receivers, and the simulation configuration to the acous-tics simulator in real-time. The simulator deals with fur-ther processing and sound reproduction and provides areturn channel, which is used to transfer visual resultsback to SketchUp. Standard room acoustics parameters,such as reverberation time, clarity, strength, and soundpropagation paths (e.g. early reflections) can be visual-ized directly inside SketchUp and are updated on-the-fly.

With an auralization module that calculates spatial im-pulse responses and renders 3-D sound with realisticroom acoustics in real-time during the modeling pro-cess, SketchUp is extended to provide full room acousticsfeedback. The reproduction module supports binaural(headphones or crosstalk cancellation) and array render-ing (VBAP and Higher-Order Ambisonics). Feeding thelow latency convolution from the live input of the soundcard, the user can talk into the virtual scene with a mi-crophone, while actively modeling other sound sources,

materials and geometry at run-time. For more complexcompositions of multiple sound sources, e.g. in sound-scape applications, the audio signals can also be trans-ferred from typical digital audio workstations using thevirtual studio technology (VST) [14].

Figure 6: Computation times for early reflections based ontwo spatial hashing traversal schemes (VC and VT), for dif-ferent room models. The voxel size is varied in relation to theaverage edge length of the scene’s polygons. A minimum isfound for approximately 1.5 times the average edge length.

Performance

A detailed performance analysis of a state-of-the-art real-time implementation would be beyond the scope of thispaper, but can be found in literature [7, 8, 10]. On typi-cal commodity hardware (Quad-core CPU, 2.6 GHz), it ispossible to simulate/auralize about 10 individual soundsources, while achieving interactive update rates for theimportant direct sound (>30Hz) and early reflection fil-ters (>3Hz), and while simultaneously performing raytracing (reverberation filter updated every 4 seconds foreach of the 10 sound sources) [10]. Performance measure-ments for 1-18 sound sources are shown in Figure 7.

Figure 7: Simulation performance on a Quad-core CPU withsimultaneous real-time rendering of multiple sound sources.Interactive update rates can be maintained up to ca. 10 indi-vidual sound sources including streaming convolution.

Validation

By comparing the simulation results of the presentedlibrary with measurements of a real rooms, the imple-mented algorithms were validated. One test case werea reverberation chamber with more than ten seconds ofreverberation at low frequencies. Using a two-way studio

DAGA 2015 Nürnberg

1141

monitor and an omni-directional microphone, impulseresponses were measured and simulated at several po-sitions. The spectrogram of one measurement positionis shown in Figure 8. Although the quantization in 31third-octave bands is visible in the simulated response,the overall match is very good. Further validation resultscan be found in literature [15, 8].

Figure 8: Comparison of the spectrograms of measured (top)and simulated (bottom) impulse responses of a reverberationchamber.

Conclusion

This contribution gave insights about recent develop-ments in room acoustics simulation. A big challengeis interactive geometry, which is needed in virtual re-ality applications and interactive room acoustics design.A solution was presented by the introduction of a hy-brid spatial data structure, providing a flexible spatialhashing algorithm for low latency update of direct soundand early reflections, and high-performance binary spacepartitioning for efficient calculation of the multitude ofreflections in the late decay. Using such advanced tech-niques in combination with extensive parallel computing,i.e. multi-threading and vectorization, realizes a fully in-teractive real-time room acoustics simulation.

To increase the utilizability of such modern simulation al-gorithms, they must provide interfaces that enable com-mon users to control and access them. A researcher mostlikely uses a scientific programming language, e.g. MAT-LAB or Python, while an audio engineer is accustomedto digital audio workstations, so that interfaces to suchsoftware were presented. With the focus on room acous-tics planning, the users are architects, civil engineers,and acoustic consultants. Therefore an interface to thepopular CAD modeling tool SketchUp was introduced,offering a seamless integration of room acoustics render-ing into its main program surface. With permanent liveacoustics feedback during architectural work, it can be

expected that typically predominantly visually driven de-sign decisions experience an increased attention towardsacoustical aspects.

The requirements therefore are seamless integration,user-friendliness, high performance, and easy under-standing of the results. To the experience of the au-thors, the understanding of room acoustical phenomenais highly enhanced by the auralization technique, makingeffects directly audible in addition to the visual monitor-ing of room acoustics parameters.

References

[1] Cadet, G. & Lecussan, B.: Coupled use of BSP andBVH trees in order to exploit ray bundle performance.IEEE/EG Symposium on Interactive Ray Tracing, 2007

[2] Krokstad, A., Strom, S. & Sørsdal, S.: Calculating theacoustical room response by the use of a ray tracing tech-nique. Journal of Sound and Vibration 8(1), 118-125, 1968

[3] Allen, J. B. & Berkley, D. A.: Image method for effi-ciently simulating small?room acoustics. The Journal ofthe Acoustical Society of America, 65(4), 943-950, 1979

[4] Pelzer, S. et al.: Interactive real-time simulation and au-ralization for modifiable rooms. Building Acoustics, 21(1),65-74, 2014

[5] Schroder, D., Ryba, A., & Vorlander, M.: Spatial datastructures for dynamic acoustic virtual reality. In Proc.20th Intern. Congress on Acoustics, Sydney, 2010

[6] Wald, I. et al.: Interactive rendering with coherent raytracing. Proceedings of Eurographics 20, 153-164, 2001

[7] Pelzer, S. et al.: Integrating real-time room acoustics sim-ulation into a CAD modeling software to enhance the ar-chitectural design process. Buildings, 4(2), 113-138, 2014

[8] Schroder, D.: Physically based real-time auralization ofinteractive virtual environments (Vol. 11). Logos VerlagBerlin GmbH, 2011

[9] Wefers, F. et al.: Interactive acoustic virtual environ-ments using distributed room acoustic simulations. Proc.EAA Joint Symp. on Auralization and Ambisonics, 2014

[10] Schallenberg, R.: Multi-core Parallelism for Real-timeRoom Acoustics Simulation. Master’s thesis, RWTHAachen University, 2015

[11] Pelzer, S. et al.: 3D reproduction of room auralizationsby combining intensity panning, crosstalk cancellationand ambisonics. Proc. EAA Joint Symposium on Aural-ization and Ambisonics, 2014

[12] Schroder, D. et al.: Virtual reality system at RWTHAachen University. Proc. International Symposium onRoom Acoustics, Melbourne, Australia, 2010

[13] Trimble SketchUp Homepage, URL:http://www.sketchup.com/

[14] Steinberg Virtual Studio Technology, Plug-In Specifica-tion 2.0, SDK. Documentation Release #1, 1999

[15] Pelzer, S. et al.: Quality assessment of room acous-tic simulation tools by comparing binaural measurementsand simulations in an optimized test scenario. Proc. Fo-rum Acusticum, Aalborg, Denmark, 2011

DAGA 2015 Nürnberg

1142