Sound Field Extrapolation: Inverse Problems, Virtual Microphone Arrays, and Spatial Filters

Berry A, Camier C, Chambatte É, Gauthier PA, Pasco Y. Sound Field Extrapolation: Inverse Problems, Virtual Microphone Arrays, and Spatial Filters. In: AES Conference: 40th International Conference: Spatial Audio: Sense the Sound of Space. Audio Engineering Society; 2010. Paper 3-2. Available from: https://aes.org/publications/elibrary-page/?id=15534

@inproceedings{Berry2010_15534,
  author = {Berry, Alain and Camier, Cédric and Chambatte, Éric and Gauthier, Philippe-Aubert and Pasco, Yann},
  title = {{Sound Field Extrapolation: Inverse Problems, Virtual Microphone Arrays, and Spatial Filters}},
  booktitle = {AES Conference: 40th International Conference: Spatial Audio: Sense the Sound of Space},
  note = {Paper 3-2},
  year = {2010},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=15534}
}

TY  - CPAPER
TI  - Sound Field Extrapolation: Inverse Problems, Virtual Microphone Arrays, and Spatial Filters
AU  - Berry, Alain
AU  - Camier, Cédric
AU  - Chambatte, Éric
AU  - Gauthier, Philippe-Aubert
AU  - Pasco, Yann
T2  - AES Conference: 40th International Conference: Spatial Audio: Sense the Sound of Space
M1  - Paper 3-2
PY  - 2010
DA  - 2010/10/06
UR  - https://aes.org/publications/elibrary-page/?id=15534
PB  - Audio Engineering Society
LA  - en
AB  - Sound field extrapolation is useful for measurement, description and characterization of sound environments and sound fields that must be reproduced using spatial sound system such as Wave Field Synthesis, Ambisonics, etc. In this paper, two methods are compared: inverse problems and virtual microphone arrays with filtering in the cylindrical harmonics domain. The goal was to define and identify methods that could accommodate to various non-uniform sensor arrays (i.e. non array-specific methods) and that are less sensitive to measurement noise. According to the results presented in this paper, it seems that the method based on inverse problem with Tikhonov regularization is less sensitive to measurement noise.
ER  -