Microphone Subset Selection for MVDR Beamformer Based Noise Reduction

Jie Zhang, Sundeep Prabhakar Chepuri, Richard Christian Hendriks, Richard Heusdens

Research output: Contribution to journalArticlepeer-review

45 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)550-563
Number of pages14
JournalIEEE/ACM Transactions on Audio Speech and Language Processing
Issue number3
StatePublished - Dec 22 2017
Externally publishedYes

ASJC Scopus subject areas

  • Media Technology
  • Instrumentation
  • Acoustics and Ultrasonics
  • Linguistics and Language
  • Signal Processing
  • Electrical and Electronic Engineering
  • Speech and Hearing


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