Q. Zhu, P. Coleman, M. Wu, and J. Yang, “Robust Personal Audio Reproduction Based on Acoustic Transfer Function Modelling,” in Proc. AES Conference: 2016 AES International Conference on Sound Field Control, Jul. 2016, Paper 1-4. [Online]. Available: https://aes.org/publications/elibrary-page/?id=18324
Zhu Q, Coleman P, Wu M, Yang J. Robust Personal Audio Reproduction Based on Acoustic Transfer Function Modelling. In: AES Conference: 2016 AES International Conference on Sound Field Control. Audio Engineering Society; 2016. Paper 1-4. Available from: https://aes.org/publications/elibrary-page/?id=18324
@inproceedings{Zhu2016_18324,
author = {Zhu, Qiaoxi and Coleman, Philip and Wu, Ming and Yang, Jun},
title = {{Robust Personal Audio Reproduction Based on Acoustic Transfer Function Modelling}},
booktitle = {AES Conference: 2016 AES International Conference on Sound Field Control},
note = {Paper 1-4},
year = {2016},
month = jul,
publisher = {Audio Engineering Society},
url = {https://aes.org/publications/elibrary-page/?id=18324}
}
TY - CPAPER
TI - Robust Personal Audio Reproduction Based on Acoustic Transfer Function Modelling
AU - Zhu, Qiaoxi
AU - Coleman, Philip
AU - Wu, Ming
AU - Yang, Jun
T2 - AES Conference: 2016 AES International Conference on Sound Field Control
M1 - Paper 1-4
PY - 2016
DA - 2016/07/06
UR - https://aes.org/publications/elibrary-page/?id=18324
PB - Audio Engineering Society
LA - en
AB - Personal audio systems generate a local sound field for a listener while attenuating the sound energy at pre-defined quiet zones. Their performance can be sensitive to errors in the acoustic transfer functions between the sources and the zones. In this paper, we model the loudspeakers as a superposition of multipoles with a term to describe errors in the actual gain and phase. We then propose a design framework for robust reproduction, incorporating additional prior knowledge about the error distribution where available. We combine acoustic contrast control with worst-case and probability-model optimization, exploiting knowledge of the error distribution. Monte-Carlo simulations over 10000 test cases show that the method increases system robustness when errors are present in the assumed transfer functions.
ER -
