Beamforming: a versatile approach to spatial filtering

Veen, B.D. Van; Buckley, K.M.

doi:10.1109/53.665

Cited by 3,041 publications

(1,583 citation statements)

References 52 publications

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“…One prominent class of techniques, known as adaptive spatial filtering, was originally developed in the field of array signal processing (van Veen and Buckley, 1988). A wellknown example of this class, the minimum-variance spatial filter, has been successfully applied to MEG/EEG source reconstruction problems (Robinson and Vrba, 1999;Sekihara and Scholz, 1996;Sekihara et al, 2001Sekihara et al, , 2002avan Veen et al, 1997).…”

Section: Introductionmentioning

confidence: 99%

Localization bias and spatial resolution of adaptive and non-adaptive spatial filters for MEG source reconstruction

2005

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This paper discusses the location bias and the spatial resolution in the reconstruction of a single dipole source by various spatial filtering techniques used for neuromagnetic imaging. We first analyze the location bias for several representative adaptive and non-adaptive spatial filters using their resolution kernels. This analysis theoretically validates previously reported empirical findings that standardized low-resolution electromagnetic tomography (sLORETA) has no location bias. We also find that the minimum-variance spatial filter does exhibit bias in the reconstructed location of a single source, but that this bias is eliminated by using the normalized lead field. We then focus on the comparison of sLORETA and the lead-field normalized minimum-variance spatial filter, and analyze the effect of noise on source location bias. We find that the signal-to-noise ratio (SNR) in the measurements determines whether the sLORETA reconstruction has source location bias, while the lead-field normalized minimum-variance spatial filter has no location bias even in the presence of noise. Finally, we compare the spatial resolution for sLORETA and the minimum-variance filter, and show that the minimum-variance filter attains much higher resolution than sLORETA does. The results of these analyses are validated by numerical experiments as well as by reconstructions based on two sets of evoked magnetic responses.

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Section: Introductionmentioning

confidence: 99%

Localization bias and spatial resolution of adaptive and non-adaptive spatial filters for MEG source reconstruction

2005

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“…The initial DOA estimates (one per TF bin) can be obtained by any SS DOA estimator such as PIV [10], as used in this work, Augmented Intensity Vectors (AIVs) [12], [13], Steered Response Power-based methods [14] or SS MUSIC. The initial DOA estimates are weighted based on their consistency within a time interval and the ones with the strongest weights are selected as the most consistent DOAs using the assumption of stationary sources.…”

Section: A Msecmentioning

confidence: 99%

Multiple DOA estimation based on estimation consistency and spherical harmonic multiple signal classification

Hafezi

Moore

Naylor

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-A common approach to multiple Direction-ofArrival (DOA) estimation of speech sources is to identify TimeFrequency (TF) bins with dominant Single Source (SS) and apply DOA estimation such as Multiple Signal Classification (MUSIC) only on those TF bins. In the state-of-the-art Direct Path Dominance (DPD)-MUSIC, the covariance matrix, used as the input to MUSIC, is calculated using only the TF bins over a local TF region where only a SS is dominant. In this work, we propose an alternative approach to MUSIC in which all the SS-dominant TF bins for each speaker across TF domain are globally used to improve the quality of covariance matrix for MUSIC. Our recently proposed Multi-Source Estimation Consistency (MSEC) technique, which exploits the consistency of initial DOA estimates within a time frame based on adaptive clustering, is used to estimate the SS-dominant TF bins for each speaker. The simulation using spherical microphone array shows that our proposed MSEC-MUSIC significantly outperforms the state-of-the-art DPD-MUSIC with less than 6.5°mean estimation error and strong robustness to widely varying source separation for up to 5 sources in the presence of realistic reverberation and sensor noise.

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“…The optimum weight vector  of MVDR beamforming is determined by solving the criterion of minimizing the output power while preserving the target speech signal such as [2] :…”

Section: Minimum Variance Distortionless Response (Mvdr)mentioning

confidence: 99%

“…For example, the linearly constrained minimum variance (LCMV) beamforming has a criterion of minimizing total signal power while preserving the target signal and eliminating interference signal [2] . Due to an additional constraint to the direction of interference signal, however, its noise reduction performance is poor.…”

Section: Introductionmentioning

confidence: 99%

A Novel Covariance Matrix Estimation Method for MVDR Beamforming In Audio-Visual Communication Systems

You¹

2014

The Journal Of The Acoustical Society Of Korea

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This paper proposes a novel covariance matrix estimation scheme for minimum variance distortionless response (MVDR) beamforming. By accurately tracking direction-of-sound source arrival (DoA) information using audio-visual sensors, the covariance matrix is efficiently estimated by adopting a variable forgetting factor. The variable forgetting factor is determined by considering signal-to-interference ratio (SIR). Experimental results verify that the performance of the proposed method is superior to that of the conventional one in terms of interference/noise reduction and speech distortion.

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Beamforming: a versatile approach to spatial filtering

Cited by 3,041 publications

References 52 publications

Localization bias and spatial resolution of adaptive and non-adaptive spatial filters for MEG source reconstruction

Localization bias and spatial resolution of adaptive and non-adaptive spatial filters for MEG source reconstruction

Multiple DOA estimation based on estimation consistency and spherical harmonic multiple signal classification

A Novel Covariance Matrix Estimation Method for MVDR Beamforming In Audio-Visual Communication Systems

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