A compact noise covariance matrix model for MVDR beamforming
File(s)
Author(s)
H. Moore, Alastair
Hafezi, Sina
R. Vos, Rebecca
A. Naylor, Patrick
Brookes, Mike
Type
Journal Article
Abstract
Acoustic beamforming is routinely used to improve the SNR of the received signal in applications such as hearing aids, robot audition, augmented reality, teleconferencing, source localisation and source tracking. The beamformer can be made adaptive by using an estimate of the time-varying noise covariance matrix in the spectral domain to determine an optimised beam pattern in each frequency bin that is specific to the acoustic environment and that can respond to temporal changes in it. However, robust estimation of the noise covariance matrix remains a challenging task especially in non-stationary acoustic environments. This paper presents a compact model of the signal covariance matrix that is defined by a small number of parameters whose values can be reliably estimated. The model leads to a robust estimate of the noise covariance matrix which can, in turn, be used to construct a beamformer. The performance of beamformers designed using this approach is evaluated for a spherical microphone array under a range of conditions using both simulated and measured room impulse responses. The proposed approach demonstrates consistent gains in intelligibility and perceptual quality metrics compared to the static and adaptive beamformers used as baselines.
Date Issued
2022-06-07
Date Acceptance
2022-05-22
Citation
IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2022, 30, pp.2049-2061
ISSN
2329-9290
Publisher
https://ieeexplore.ieee.org/document/9789595
Start Page
2049
End Page
2061
Journal / Book Title
IEEE/ACM Transactions on Audio, Speech, and Language Processing
Volume
30
Copyright Statement
© 2022 The Author(s).
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0
License URL
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Identifier
https://ieeexplore.ieee.org/document/9789595
Grant Number
EP/S035842/1
Subjects
Science & Technology
Technology
Acoustics
Engineering, Electrical & Electronic
Engineering
Covariance matrices
Signal to noise ratio
Microphone arrays
Estimation
Array signal processing
Time-frequency analysis
Beamforming
speech enhancement
covariance matrix estimation
spatial filtering
spherical microphone arrays
adaptive beamforming
microphone array
MVDR
MPDR
SPEECH ENHANCEMENT
JOINT ESTIMATION
POWER
REDUCTION
ALGORITHM
DENSITY
Publication Status
Published
Date Publish Online
2022-06-07