The Visual Computer

, Volume 25, Issue 5–7, pp 469–477 | Cite as

Realistic real-time sound re-synthesis and processing for interactive virtual worlds

Original Article


We present new GPU-based techniques for implementing linear digital filters for real-time audio processing. Our solution for recursive filters is the first presented in the literature. We demonstrate the relevance of these algorithms to computer graphics by synthesizing realistic sounds of colliding objects made of different materials, such as glass, plastic, and wood, in real time. The synthesized sounds can be parameterized by the object materials, velocities, and collision angles. Despite its flexibility, our approach uses very little memory, since it essentially requires a set of coefficients representing the impulse response of each material sound. Such features make our approach an attractive alternative to traditional CPU-based techniques that use playback of pre-recorded sounds.


Recursive filters Real-time audio processing Linear digital filters GPU-based techniques 


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  1. 1.
    Ansari, M.: Video image processing using shaders. In: Game Developers Conference (2003) Google Scholar
  2. 2.
    Antoniou, A.: Digital Filters: Analysis and Design. McGraw-Hill, New York (1980) Google Scholar
  3. 3.
    ASIO. All you need to know about ASIO. Available on-line at articleID=53937&categoryID=15. Last access: Dec 2008
  4. 4.
    Barcellos, A.: Alfred’s world 3D physics engine. Available on-line at Last access: Jan 2009
  5. 5.
    Begault, D.R.: 3-D Sound for Virtual Reality and Multimedia. Academic Press, Diego (1994) Google Scholar
  6. 6.
    Bierens, L., Deprettere, E.: Efficient partitioning of algorithms for long convolutions and their mapping onto architectures. J. VLSI Signal Process. Syst. 18(1), 51–64 (1998) Google Scholar
  7. 7.
    Bonneel, N., Drettakis, G., Tsingos, N., Viaud-Delmon, I., James, D.: Fast modal sounds with scalable frequency-domain synthesis. In: SIGGRAPH ’08: ACM SIGGRAPH 2008 papers, pp. 1–9 (2008) Google Scholar
  8. 8.
    Delsarte, P., Genin, Y.: The split Levinson algorithm. In: IEEE Transactions on Acoustics, Speech and Signal Processing, pp. 470–478 (1986) Google Scholar
  9. 9.
    Eyre, J., Bier, J.: The evolution of dsp processors. IEEE Signal Process. Mag. 17(2), 43–51 (2000) CrossRefGoogle Scholar
  10. 10.
    Gallo, E., Drettakis, G.: Breaking the 64 spatialized sources barrier (2003) Google Scholar
  11. 11.
    Gallo, E., Tsingos, N.: Efficient 3d audio processing with the gpu. In: Electronic Proceedings of the ACM Workshop on General Purpose Computing on Graphics Processors, pp. c-42 (2004) Google Scholar
  12. 12.
    García, G.: Optimal filter partition for efficient convolution with short input/output delay. In: Proceedings of the Audio Engineering Society, no. 113 (2002) Google Scholar
  13. 13.
    Govindaraju, N.K., Manocha, D.: Cache-efficient numerical algorithms using graphics hardware. Parallel Comput. 33(10–11), 663–684 (2007) Google Scholar
  14. 14.
    Harrington, J., Cassidy, S.: Techniques in Speech Acoustics, pp. 211–238. Kluwer Academic, Dordrecht (1999) Google Scholar
  15. 15.
    Harris, M.: Mapping computational concepts to GPUs. In: Pharr, M. (ed.) GPU Gems 2, pp. 493–508. Addison Wesley, Reading (2005) Google Scholar
  16. 16.
    Houston, M., Govindaraju, N.: Gpgpu course (2007) Google Scholar
  17. 17.
    Jargstorff, F.: A framework for image processing. In: Fernando, R. (ed.) GPU Gems, pp. 445–467. Addison Wesley, Reading (2004) Google Scholar
  18. 18.
    Jedrzejewski, M., Marasek, K.: Computation of room acoustics using programmable video hardware. In: International Conference on Computer Vision and Graphics ICCVG’2004 (2004) Google Scholar
  19. 19.
    Moreland, K., Angel, E.: The fft on a gpu. In: HWWS ’03: Proceedings of the ACM SIGGRAPH/EUROGRAPHICS Conference on Graphics Hardware, pp. 112–119. Eurographics Association (2003) Google Scholar
  20. 20.
    Nickolls, J., Buck, I., Garland, M., Skadron, K.: Scalable parallel programming with cuda. ACM Queue 6(2), 40–53 (2008) CrossRefGoogle Scholar
  21. 21.
    NVIDIA Corporation: NVIDIA CUDA Programming Guide 2.0. NVIDIA Corporation, Santa Clara, CA, USA, July 2008. Available for free as a part of the NVIDIA CUDA Toolkit 2.0 Google Scholar
  22. 22.
    Rabiner, L., Juang, B.-H.: Fundamentals of Speech Recognition. Prentice-Hall, Upper Saddle River (1993) Google Scholar
  23. 23.
    Robelly, J., Cichon, G., Seidel, H., Fettweis, G.: Implementation of recursive digital filters into vector simd dsp architectures. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 5, pp. 165–168 (2004) Google Scholar
  24. 24.
    Spitzer, J.: Implementing a gpu-efficient fft. Nvidia course presentation at SIGGRAPH 2003 Google Scholar
  25. 25.
    Sumanaweera, T., Liu, D.: Medical Image Reconstruction with the FFT. In: Pharr, M. (ed.) GPU Gems 2, pp. 765–784. Addison Wesley, Reading (2005) Google Scholar
  26. 26.
    Trebien, F., Oliveira, M.M.: ShaderX6: Advanced Rendering. In: Real-time audio processing on the gpu, pp. 583–604. Charles River Media, Hingham (2008) Google Scholar
  27. 27.
    Zhang, Q., Ye, L., Pan, Z.: Physically-based sound synthesis on GPUs. In: Entertainment Computing, ICEC 2005. LNCS, pp. 328–333. Springer, Berlin (2005) CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  1. 1.Instituto de InformáticaUFRGSPorto AlegreBrazil

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