SVD-Domain Basis Vector Interpolation and Bidirectional Cascaded Long Term Prediction for Frame Loss Concealment in Higher Order Ambisonics Signals

Namazi M, Elshafiy A, Rose K. SVD-Domain Basis Vector Interpolation and Bidirectional Cascaded Long Term Prediction for Frame Loss Concealment in Higher Order Ambisonics Signals. In: AES Convention 155. Audio Engineering Society; 2023. Paper 174. Available from: https://aes.org/publications/elibrary-page/?id=22328

@inproceedings{Namazi2023_22328,
  author = {Namazi, Mahmoud and Elshafiy, Ahmed and Rose, Kenneth},
  title = {{SVD-Domain Basis Vector Interpolation and Bidirectional Cascaded Long Term Prediction for Frame Loss Concealment in Higher Order Ambisonics Signals}},
  booktitle = {AES Convention 155},
  note = {Paper 174},
  year = {2023},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=22328}
}

TY  - CPAPER
TI  - SVD-Domain Basis Vector Interpolation and Bidirectional Cascaded Long Term Prediction for Frame Loss Concealment in Higher Order Ambisonics Signals
AU  - Namazi, Mahmoud
AU  - Elshafiy, Ahmed
AU  - Rose, Kenneth
T2  - AES Convention 155
M1  - Paper 174
PY  - 2023
DA  - 2023/10/06
UR  - https://aes.org/publications/elibrary-page/?id=22328
PB  - Audio Engineering Society
LA  - en
AB  - This paper proposes a novel frame loss concealment technique for higher order ambisonics (HOA) audio signals. It is designed to overcome the challenge of interpolating lost frames of HOA data and recover a close approximation of the original data without significantly impacting it’s localization. The underlying idea uses two techniques. The first is cascaded long term prediction, a technique which uses a cascade of long-term prediction filters to capture periodic components of music signals, to predict the lost frame’s ambisonics channels, in the SVD domain, from the periodic components of the past and future frames. Additionally, spherical linear interpolation of the SVD basis vectors is used to accurately reconstruct the spatialization of the lost frame. Objective and subjective evaluations show this method to be superior in accurately reconstructing lost frames to cascaded long term prediction being applied directly to the ambisonics signal.
ER  -