Headphone-Based Virtual Spatialization of Sound with a GPU Accelerator

Belloch JA, Ferrer M, Gonzalez A, Martinez-Zaldivar FJ, Vidal AM. Headphone-Based Virtual Spatialization of Sound with a GPU Accelerator. J Audio Eng Soc. 2013;61(7/8):546-561. Available from: https://aes.org/publications/elibrary-page/?id=16870

@article{Belloch2013_16870,
  author = {Belloch, Jose A. and Ferrer, Miguel and Gonzalez, Alberto and Martinez-Zaldivar, F.J. and Vidal, Antonio M.},
  title = {{Headphone-Based Virtual Spatialization of Sound with a GPU Accelerator}},
  journal = {Journal of the Audio Engineering Society},
  volume = {61},
  number = {7/8},
  pages = {546--561},
  year = {2013},
  month = jul,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=16870}
}

TY  - JOUR
TI  - Headphone-Based Virtual Spatialization of Sound with a GPU Accelerator
AU  - Belloch, Jose A.
AU  - Ferrer, Miguel
AU  - Gonzalez, Alberto
AU  - Martinez-Zaldivar, F.J.
AU  - Vidal, Antonio M.
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 61
IS  - 7/8
SP  - 546
EP  - 561
PY  - 2013
DA  - 2013/07/06
UR  - https://aes.org/publications/elibrary-page/?id=16870
PB  - Audio Engineering Society
LA  - en
AB  - This paper describes the design of a binaural headphone-based multisource spatial-audio application using a Graphical Processing Unit (GPU) as the compute engine. It is a highly parallel programmable coprocessor that provides massive computation power when the algorithm is properly parallelized. To render a sound source at a specific location, audio samples must be convolved with Head Related Impulse Responses (HRIR) filters for that location. A data base of HRIR at fixed spatial positions is used. Solutions have been developed to handle two problems: synthesizing sound sources positions that are not in the HRIR database, and virtualizing the movement of the sound sources between different positions. The GPU is particularly appropriate for simultaneously executing multiple convolutions without overloading the main CPU. The results show that the proposed application is able to handle up to 240 sources simultaneously when all sources are moving.
ER  -