TY - JOUR
T1 - Microphone Subset Selection for MVDR Beamformer Based Noise Reduction
AU - Zhang, Jie
AU - Chepuri, Sundeep Prabhakar
AU - Hendriks, Richard Christian
AU - Heusdens, Richard
PY - 2018
Y1 - 2018
N2 - In large-scale wireless acoustic sensor networks (WASNs), many of the sensors will only have a marginal contribution to a certain estimation task. Involving all sensors increases the energy budget unnecessarily and decreases the lifetime of the WASN. Using microphone subset selection, also termed as sensor selection, the most informative sensors can be chosen from a set of candidate sensors to achieve a prescribed inference performance. In this paper, we consider microphone subset selection for minimum variance distortionless response (MVDR) beamformer based noise reduction. The best subset of sensors is determined by minimizing the transmission cost while constraining the output noise power (or signal-to-noise ratio). Assuming the statistical information on correlation matrices of the sensor measurements is available, the sensor selection problem for this model-driven scheme is first solved by utilizing convex optimization techniques. In addition, to avoid estimating the statistics related to all the candidate sensors beforehand, we also propose a data-driven approach to select the best subset using a greedy strategy. The performance of the greedy algorithm converges to that of the model-driven method, while it displays advantages in dynamic scenarios as well as on computational complexity. Compared to a sparse MVDR or radius-based beamformer, experiments show that the proposed methods can guarantee the desired performance with significantly less transmission costs.
AB - In large-scale wireless acoustic sensor networks (WASNs), many of the sensors will only have a marginal contribution to a certain estimation task. Involving all sensors increases the energy budget unnecessarily and decreases the lifetime of the WASN. Using microphone subset selection, also termed as sensor selection, the most informative sensors can be chosen from a set of candidate sensors to achieve a prescribed inference performance. In this paper, we consider microphone subset selection for minimum variance distortionless response (MVDR) beamformer based noise reduction. The best subset of sensors is determined by minimizing the transmission cost while constraining the output noise power (or signal-to-noise ratio). Assuming the statistical information on correlation matrices of the sensor measurements is available, the sensor selection problem for this model-driven scheme is first solved by utilizing convex optimization techniques. In addition, to avoid estimating the statistics related to all the candidate sensors beforehand, we also propose a data-driven approach to select the best subset using a greedy strategy. The performance of the greedy algorithm converges to that of the model-driven method, while it displays advantages in dynamic scenarios as well as on computational complexity. Compared to a sparse MVDR or radius-based beamformer, experiments show that the proposed methods can guarantee the desired performance with significantly less transmission costs.
KW - convex optimization
KW - greedy algorithm
KW - MVDR
KW - noise reduction
KW - Sensor selection
KW - sparsity
KW - transmission power
UR - http://www.scopus.com/inward/record.url?scp=85039775307&partnerID=8YFLogxK
U2 - 10.1109/TASLP.2017.2786544
DO - 10.1109/TASLP.2017.2786544
M3 - Article
AN - SCOPUS:85039775307
SN - 2329-9290
VL - 26
SP - 550
EP - 563
JO - IEEE - ACM Transactions on Audio, Speech, and Language Processing
JF - IEEE - ACM Transactions on Audio, Speech, and Language Processing
IS - 3
M1 - 8234698
ER -