Ambisonic Source Estimation using Sparse Bayesian Learning and Broadband Feature Selection

Tung A, Gerstoft P. Ambisonic Source Estimation using Sparse Bayesian Learning and Broadband Feature Selection. In: AES 2024 International Conference on Audio for Virtual and Augmented Reality. Audio Engineering Society; 2024. Paper 8. Available from: https://aes.org/publications/elibrary-page/?id=22657

@inproceedings{Tung2024_22657,
  author = {Tung, Alex and Gerstoft, Peter},
  title = {{Ambisonic Source Estimation using Sparse Bayesian Learning and Broadband Feature Selection}},
  booktitle = {AES 2024 International Conference on Audio for Virtual and Augmented Reality},
  note = {Paper 8},
  year = {2024},
  month = aug,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=22657}
}

TY  - CPAPER
TI  - Ambisonic Source Estimation using Sparse Bayesian Learning and Broadband Feature Selection
AU  - Tung, Alex
AU  - Gerstoft, Peter
T2  - AES 2024 International Conference on Audio for Virtual and Augmented Reality
M1  - Paper 8
PY  - 2024
DA  - 2024/08/05
UR  - https://aes.org/publications/elibrary-page/?id=22657
PB  - Audio Engineering Society
LA  - en
AB  - A method of acoustic holography is introduced by expanding the equivalent source method (ESM) to include multipole objects under the higher-order Ambisonic (HOA) paradigm and obtaining an optimal solution in a two-stage manner: Sparse Bayesian learning (SBL) is first used to estimate a sparse set of broadband spherical harmonic source strengths, followed by a frequency dependent refinement over the identified source set via a dimensionally reduced inversion. The soundfield is able to be sufficiently reconstructed from the final source estimate, which can be stored, manipulated, and rendered using Ambisonic techniques. The method is used to estimate a simulated quadrupole of finite dimension, and the reconstruction accuracy using several arrangements of spherical microphone arrays is compared.
ER  -