Extended Energy Vector Prediction of Ambisonically Reproduced Image Direction at Off-Center Listening Positions

Stitt P, Bertet S, van Walstijn M. Extended Energy Vector Prediction of Ambisonically Reproduced Image Direction at Off-Center Listening Positions. J Audio Eng Soc. 2016;64(5):299-310. doi:10.17743/jaes.2016.0008

@article{Stitt2016_18135,
  author = {Stitt, Peter and Bertet, Stéphanie and van Walstijn, Maarten},
  title = {{Extended Energy Vector Prediction of Ambisonically Reproduced Image Direction at Off-Center Listening Positions}},
  journal = {Journal of the Audio Engineering Society},
  volume = {64},
  number = {5},
  pages = {299--310},
  year = {2016},
  month = may,
  publisher = {Audio Engineering Society},
  doi = {10.17743/jaes.2016.0008},
  url = {https://doi.org/10.17743/jaes.2016.0008}
}

TY  - JOUR
TI  - Extended Energy Vector Prediction of Ambisonically Reproduced Image Direction at Off-Center Listening Positions
AU  - Stitt, Peter
AU  - Bertet, Stéphanie
AU  - van Walstijn, Maarten
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 64
IS  - 5
SP  - 299
EP  - 310
PY  - 2016
DA  - 2016/05/06
DO  - 10.17743/jaes.2016.0008
UR  - https://doi.org/10.17743/jaes.2016.0008
PB  - Audio Engineering Society
LA  - en
AB  - Spatial audio techniques, such as Ambisonics and Wave Field Synthesis, aim to reproduce the sound field in the limited area of the sweet spot, which is approximately equidistant from all loudspeakers. This research extends the energy vector technique to improved localization at off-centered positions. In determining the source direction, a perceptual weight is assigned to each loudspeaker gain that takes into account the relative arrival times, levels, and directions of the loudspeaker signals. Conversely, uncompensated differences in arrival time can trigger the precedence effect. The proposed model was evaluated and compared to the original energy vector model and two binaural models. The extended energy vector version was at least 50% more accurate than the second best predictor with an average error of about 4°.
ER  -