Computation of room acoustics using programable video hardware

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Computation of room acoustics using programable
video hardware

• Marcin Jedrzejewski Krzysztof Marasek

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Presentation plan

• Acoustics computational methods
• Graphical Processing Unit ( GPU ) programming
  model
• Used acoustic model
• Used algorithms and data structures
• Implementation on GPU
• Results
• Conclusions and further work
• Demo movie

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Room acoustics

• - Image sources
• Computationally ineffective

• Geometrical methods
• - Beam tracing
• Not very scaleable
• Ray tracing
• Point sound source
• Receiver aproximated by sphere
• Each position change requires
• recomputation

Echogram
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Room representation

• 3D geometry
• Walls are made of polygons
• Each wall contains information on its absorption
  coeficient
• Scene contains also positions of sound source
• and receiver

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GPU programming model

• Streaming processor - the same program is
  executed parallely but with different data on
  input. Each program produces output data.
• Programs that are executed are also known as
  kernels or Pixel Shaders
• HLSL as programming language, very similar to C
• Input and output data is stored in a form of
  textures which are blocks of memory stored on
  video card

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Program execution

• Data (like rays) are loaded to input texture
• Quad (rectangle composed of two triangles) is
  rendered, for each processed texel, pixel shader
  is executed
• HLSL program can read from many different
  textures but can write up to 16 floating point
  values (using MRT)
• Many passes of this algorithm

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Acoustics model

• One frequency band is used for
• material absorption
• High number of reflections (15 - 25)
• No diffraction, refraction, diffusion
• purely specular model

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Space division algorithms used

• Constructive Solid Geometry (CSG) to remove all
  illegal geometry
• Binary Space Partitioning (BSP) to partition
  space into
• convex subspaces
• Portal calculation to find ways
• between subspaces
• Further subspaces division
• required for efficient GPU
• implementation

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Execution flow

1. CSG, BSP, Portals, ...
2. Generation of rays on sphere surface, uploading
   textures to video card
3. Propagation of rays through portals and
   reflecting them from walls
4. Retrieving video memory with computed rays and
   building echogram
5. Using echogram to generate spatial sound

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Results

• Almost 100 mln ray - triangle intersections
  checks per second
• Computation of above 16000 rays with 10
  reflections in two room enviroment takes 30ms

Model Poly count Precomputation CPU GPU
Two rooms 24 1,4s 0,5s 0,016s
House 192 1,89s 0,37s 0,025s
Office 390 4,54s 0,82s 0,027s
Church 390 1,09s 0,82s 0,033s
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Conclusions and further work

• Real-time computation of echogram with the use of
  ray tracing algorithm
• CPU can execute other code parallely when GPU is
  computing ray reflections
• Making use of NVIDIA Geforce 6800 or ATI X800
  cards
• Making use of PCI-Express architecture (even 16ms
  speed up possible)
• Mapping cone or pyramid tracing algorithms for
  early reflections and using raytracing for late
  reverberation
• Better optimizations with model 3 of PS and VS

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Demo movie