Abstract
Personal sound zone systems require to know the acoustic transfer functions (ATFs) between the loudspeakers and matching microphones, which in general have to be measured in advance. As the measurement is affected by such factors as sensors' position mismatches and environmental changes, deviation of the measured ATF-s from the true ones is unavoidable and such perturbation may lead to significant performance degradation in sound zone production. In this work, a method is developed for robust acoustic contrast control, which simultaneously considers the norm-bounded uncertainties and a positive semidefinite constraint to ensure the optimization problem to have a solution. Since the problem is non-convex, the polynomial time difference-of-convex functions (POTDC) algorithm is adopted to identify the optimal solution. Simulations are performed to validate the effectiveness of the proposed method and it demonstrates a performance superior to other existing robust control methods.
Original language | English |
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Title of host publication | International Workshop on Acoustic Signal Enhancement, IWAENC 2022 - Proceedings |
Publisher | IEEE |
Publication date | 2022 |
Article number | 9914730 |
ISBN (Print) | 978-1-6654-6868-8 |
ISBN (Electronic) | 978-1-6654-6867-1 |
DOIs | |
Publication status | Published - 2022 |
Event | 17th International Workshop on Acoustic Signal Enhancement, IWAENC 2022 - Bamberg, Germany Duration: 5 Sept 2022 → 8 Sept 2022 |
Conference
Conference | 17th International Workshop on Acoustic Signal Enhancement, IWAENC 2022 |
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Country/Territory | Germany |
City | Bamberg |
Period | 05/09/2022 → 08/09/2022 |
Sponsor | et al., Head Acoustics, Huawei Technologies Co., Ltd., iFlytek, Reality Labs Research, WSAudiology |
Bibliographical note
Funding agency:10.13039/100006190-Research and Development
10.13039/100000001-National Science Foundation
Publisher Copyright:
© 2022 IEEE.
Keywords
- Acoustic contrast control
- personal sound zone
- polynomial time difference-of-convex functions (POTDC)
- positive semidefinite constraint
- robustness