Abstract
In this paper we propose a distributed reformulation of the linearly constrained minimum variance (LCMV) beamformer for use in acoustic wireless sensor networks. The proposed distributed minimum variance (DMV) algorithm, for which we demonstrate implementations for both cyclic and acyclic networks, allows the optimal beamformer output to be computed at each node without the need for sharing raw data within the network. By exploiting the low rank structure of estimated covariance matrices in time-varying noise fields, the algorithm can also provide a reduction in the total amount of data transmitted during computation when compared to centralised solutions. This is particularly true when multiple microphones are used per node. We also compare the performance of DMV with state of the art distributed beamformers and demonstrate that it achieves greater improvements in SNR in dynamic noise fields with similar transmission costs.
Original language | English |
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Title of host publication | 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
Subtitle of host publication | Proceedings |
Editors | Min Dong, Thomas Fang Zheng |
Place of Publication | Danvers, MA |
Publisher | IEEE |
Pages | 101-105 |
Number of pages | 5 |
ISBN (Electronic) | 978-1-4799-9988-0 |
DOIs | |
Publication status | Published - 19 May 2016 |
Event | 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai International Convention Center, Shanghai, China Duration: 20 Mar 2016 → 25 Mar 2016 |
Conference
Conference | 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 |
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Abbreviated title | ICASSP |
Country/Territory | China |
City | Shanghai |
Period | 20/03/16 → 25/03/16 |
Bibliographical note
Accepted Author ManuscriptKeywords
- beamforming
- Distributed
- LCMV
- acoustic