DIRECTIONAL GAIN BASED NOISE COVARIANCE MATRIX ESTIMATION FOR MVDR BEAMFORMING

Fan Zhang, Chao Pan, Jacob Benesty, Jingdong Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

This paper is devoted to the problem of noise covariance matrix (NCM) estimation. It proposes a time-frequency masking based approach. We first present an optimal mask function based on the mean-squared error criterion. To estimate this mask, we employ the recently developed directional gain method based on the knowledge of the signal incident angle. To demonstrate the effectiveness of the proposed NCM estimator, we integrate it into the minimum variance distortionless response (MVDR) beamformer. The speech enhancement results in noise-plus-interference environments show the advantages of the proposed method over two baseline beamforming algorithms.

Original languageEnglish
Title of host publication2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages511-515
Number of pages5
ISBN (Electronic)9798350344851
DOIs
StatePublished - 2024
Event2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 - Seoul, Korea, Republic of
Duration: 14 Apr 202419 Apr 2024

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024
Country/TerritoryKorea, Republic of
CitySeoul
Period14/04/2419/04/24

Keywords

  • MVDR beamformer
  • Noise covariance matrix estimation
  • directional gain
  • speech enhancement
  • time-frequency mask

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