Spatially Robust GSC Beamforming with Controlled White Noise Gain

Bamov, Anna; Bracha, Vered Bar; Markovich‐Golan, Shmulik; Gannot, Sharon

doi:10.1109/iwaenc.2018.8521405

Cited by 7 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed GSC-SCCFC algorithm is compared with different existing algorithms like Combined adaptive beamforming [19], GSC with improved linear prediction [20], GSC with controlled white Gaussian [21], combined beamforming and echo cancellation [22]…”

Section: Resultsmentioning

confidence: 99%

“…Another existing algorithm for noise reduction in recent times is, GSC beamformer with linear prediction filter [20] which is used in multichannel speech enhancement system addresses dereverberation and noise reduction, but has high computation complexity. Barnov et al, in 2019 introduced GSC beamforming using controlled white Gaussian gain [21], where non-stationary environments are only limited to a single speaker. A modified change prediction [22] to GSC beamforming is applied which holds good for echo cancellation but fails in interference suppression.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Signed Convex Combination of Fast Convergence Algorithm to Generalized Sidelobe Canceller Beamformer for Multi-Channel Speech Enhancement

S¹,

T²

2021

View full text Add to dashboard Cite

In speech communication applications such as teleconferences, mobile phones, etc., the real-time noises degrade the desired speech quality and intelligibility. For these applications, in the case of multichannel speech enhancement, the adaptive beamforming algorithms play a major role compared to fixed beamforming algorithms. Among the adaptive beamformers, Generalized Sidelobe Canceller (GSC) beamforming with Least Mean Square (LMS) Algorithm has the least complexity but provides poor noise reduction whereas GSC beamforming with Combined LMS (CLMS) algorithm has better noise reduction performance but with high computational complexity. In order to achieve a tradeoff between noise reduction and computational complexity in real-time noisy conditions, a Signed Convex Combination of Fast Convergence (SCCFC) algorithm based GSC beamforming for multi-channel speech enhancement is proposed. This proposed SCCFC algorithm is implemented using a signed convex combination of two Fast Convergence Normalized Least Mean Square (FCNLMS) adaptive filters with different step-sizes. This improves the overall performance of the GSC beamformer in real-time noisy conditions as well as reduces the computation complexity when compared to the existing GSC algorithms. The performance of the proposed multi-channel speech enhancement system is evaluated using the standard speech processing performance metrics. The simulation results demonstrate the superiority of the proposed GSC-SCCFC beamformer over the traditional methods.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Signed Convex Combination of Fast Convergence Algorithm to Generalized Sidelobe Canceller Beamformer for Multi-Channel Speech Enhancement

S¹,

T²

2021

View full text Add to dashboard Cite

show abstract

“…Other arrangements are useful for specific case studies, however, they are less useful for detailed analyses of spatial processors, e.g., their robustness to small perturbations in parameters [6], [7]. To analyze such details, a database containing dense measurements of RIRs must be realized, which is a cumbersome task as the measurements must be precisely repeated for different settings.…”

Section: Introductionmentioning

confidence: 99%

MIRaGe: Multichannel Database Of Room Impulse Responses Measured On High-Resolution Cube-Shaped Grid In Multiple Acoustic Conditions

Cmejla¹,

Kounovsky²,

Gannot³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

We introduce a database of multi-channel recordings performed in an acoustic lab with adjustable reverberation time. The recordings provide information about room impulse responses (RIR) for various positions of a loudspeaker. In particular, the main positions correspond to 4104 vertices of a cubeshaped dense grid within a 46×36×32 cm volume. The database thus provides a tool for detailed analyses of beampatterns of spatial processing methods as well as for training and testing of mathematical models of the acoustic field.

show abstract