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

• CSG, BSP, Portals, ...
• Generation of rays on sphere surface, uploading
  textures to video card
• Propagation of rays through portals and
  reflecting them from walls
• Retrieving video memory with computed rays and
  building echogram
• 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

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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