GPU-Based WFS Systems with Mobile Virtual Sound Sources and Room Compensation

Belloch JA, Ferrer M, Gonzalez A, Lorente J, Vidal AM. GPU-Based WFS Systems with Mobile Virtual Sound Sources and Room Compensation. In: AES Conference: 52nd International Conference: Sound Field Control - Engineering and Perception. Audio Engineering Society; 2013. Paper P-1. Available from: https://aes.org/publications/elibrary-page/?id=16896

@inproceedings{Belloch2013_16896,
  author = {Belloch, Jose A. and Ferrer, Miguel and Gonzalez, Alberto and Lorente, Jorge and Vidal, Antonio M.},
  title = {{GPU-Based WFS Systems with Mobile Virtual Sound Sources and Room Compensation}},
  booktitle = {AES Conference: 52nd International Conference: Sound Field Control - Engineering and Perception},
  note = {Paper P-1},
  year = {2013},
  month = sep,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=16896}
}

TY  - CPAPER
TI  - GPU-Based WFS Systems with Mobile Virtual Sound Sources and Room Compensation
AU  - Belloch, Jose A.
AU  - Ferrer, Miguel
AU  - Gonzalez, Alberto
AU  - Lorente, Jorge
AU  - Vidal, Antonio M.
T2  - AES Conference: 52nd International Conference: Sound Field Control - Engineering and Perception
M1  - Paper P-1
PY  - 2013
DA  - 2013/09/06
UR  - https://aes.org/publications/elibrary-page/?id=16896
PB  - Audio Engineering Society
LA  - en
AB  - Wave Field Synthesis (WFS) is a spatial audio reproduction system that provides an accurate spatial sound field in a wide area. This sound field is rendered through a high number of loudspeakers to emulate virtual sound sources. WFS systems require high computational capacity since they involve multiple loudspeakers and multiple virtual sources. Furthermore improvements of the spatial audio perception imply even higher processing capacity, mainly to avoid artifacts when the virtual sources move, and compensate the room e ects at certain control points within the listening area. Graphics Processing Units (GPUs) are well-known for their potential in highly parallel data processing. In this paper, we propose a GPU implementation that yields maximum parallelism by adapting the required computations to the di erent GPU architectures (Tesla, Fermi and Kepler).
ER  -