Imperial College London
Alternate

Mr Mike Brookes

Faculty of Engineering, Department of Electrical and Electronic Engineering

Emeritus Reader
 
 
 
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Contact

 

+44 (0)20 7594 6165mike.brookes Website

 
 
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Assistant

 

Miss Vanessa Rodriguez-Gonzalez +44 (0)20 7594 6267

 
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Location

 

807aElectrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{H:2022:10.1109/taslp.2022.3180671,
author = {H, Moore A and Hafezi, S and R, Vos R and A, Naylor P and Brookes, M},
doi = {10.1109/taslp.2022.3180671},
journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing},
pages = {2049--2061},
title = {A compact noise covariance matrix model for MVDR beamforming},
url = {http://dx.doi.org/10.1109/taslp.2022.3180671},
volume = {30},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - H,Moore A
AU  - Hafezi,S
AU  - R,Vos R
AU  - A,Naylor P
AU  - Brookes,M
DO  - 10.1109/taslp.2022.3180671
EP  - 2061
PY  - 2022///
SN  - 2329-9290
SP  - 2049
TI  - A compact noise covariance matrix model for MVDR beamforming
T2  - IEEE/ACM Transactions on Audio, Speech, and Language Processing
UR  - http://dx.doi.org/10.1109/taslp.2022.3180671
UR  - https://ieeexplore.ieee.org/document/9789595
UR  - http://hdl.handle.net/10044/1/97268
VL  - 30
ER  -
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