Experimental study of robust beamforming techniques for acoustic applications

Zhao, Yingke; Jensen, Jesper Rindom; Christensen, Mads Græsbøll; Doclo, Simon; Chen, Jingdong

doi:10.1109/waspaa.2017.8170000

Cited by 8 publications

(8 citation statements)

References 24 publications

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“…This non-linear equation in the Lagrange multiplier λ can be solved, e.g., using Newton's method [39]. The solution is then used in (34), yielding the SC speech correlation vector γ SC x . Since the normalization constraint in ( 10) is typically not satisfied, resulting in a scaling inaccuracy, normalization is performed by dividing γ SC…”

Section: A Singly-constrained (Sc) Mfmvdr Filtermentioning

confidence: 99%

“…One of the most popular approaches is diagonal loading, imposing a quadratic inequality constraint on the filter vector [25]. However, since diagonal loading does not explicitly address uncertainty of the steering vector, several other approaches were proposed, e.g., by imposing (equality and/or inequality) constraints on the so-called mismatch vector, i.e., the difference between the steering vector and the presumed steering vector [26]- [34]. The robust MVDR beamformers in [28], [30] estimate the steering vector as the vector maximizing the total signal output power of the MVDR beamformer within a spherical uncertainty set.…”

Section: Introductionmentioning

confidence: 99%

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Robust Constrained MFMVDR Filters for Single-Channel Speech Enhancement Based on Spherical Uncertainty Set

Fischer

Doclo

2021

IEEE/ACM Trans. Audio Speech Lang. Process.

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Aiming at exploiting speech correlation across consecutive time-frames in the short-time Fourier transform domain, the multi-frame minimum variance distortionless response (MFMVDR) filter for single-channel speech enhancement has been proposed. The MFMVDR filter requires an accurate estimate of the normalized speech correlation vector in order to avoid speech distortion and artifacts. In this paper we investigate the potential of using robust MVDR filtering techniques to estimate the normalized speech correlation vector as the vector maximizing the total signal output power within a spherical uncertainty set, which corresponds to imposing a quadratic inequality constraint. Whereas the singlyconstrained (SC) MFMVDR filter only considers the quadratic inequality constraint to estimate the (non-normalized) speech correlation vector, the doubly-constrained (DC) MFMVDR filter integrates a linear normalization constraint into the optimization problem to directly estimate the normalized speech correlation vector. To set the upper bound of the quadratic inequality constraint for each time-frequency point, we propose to use a trained non-linear mapping function that depends on the a-priori signal-to-noise ratio (SNR). Experimental results for different speech signals, noisetypes and SNRs show that the proposed constrained approaches yield a more accurate estimate of the normalized speech correlation vector than a state-of-the-art maximum-likelihood (ML) estimator. An instrumental and a perceptual evaluation show that both constrained MFMVDR filters lead to less speech and noise distortion but a lower noise reduction than the ML-MFMVDR filter, where the DC-MFMVDR filter is preferred in terms of overall quality compared to the SC-MFMVDR and ML-MFMVDR filters.

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Section: A Singly-constrained (Sc) Mfmvdr Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Constrained MFMVDR Filters for Single-Channel Speech Enhancement Based on Spherical Uncertainty Set

Fischer

Doclo

2021

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…For the experiments conducted in this section, we have used the robust Capon beamforming (RCB) [18,19], as this method has been shown to be robust to reverberation and uncertainty in the steering vector [19]. The number of candidate directions I has been chosen to be 8 in the experiments.…”

Section: Methodsmentioning

confidence: 99%

Hearing Aid-controlled Beamformer for Binaural Speech Enhancement Using a Model-based Approach

Kavalekalam

Nielsen

Christensen

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The understanding of speech from a particular speaker in the presence of other interfering speakers can be severely degraded for a hearing impaired person. Beamforming techniques have been proven to be effective to improve the speech understanding in such scenarios. However, the number of microphones in a hearing aid (HA) is limited due to the space and power constraints present in the HA. In this paper, we propose to use an external device e.g., a microphone array, that can communicate with the HA to overcome this limitation. We propose a method to control this external device based on the look direction of the HA user. We show, by means of simulations, the robustness of the proposed method at very low SNRs in a reverberant scenario. Moreover, we have also conducted experiments that show the benefit of using this framework for binaural and monaural enhancement.

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“…The robustness of the beamformer is improved by utilizing the noise statistics in forming the spatial filter. In order to apply these methods to wideband signals such as speech signals, the signal can be transformed into the frequency domain, after which the narrowband robust methods can be applied in each subband independently [24][25][26][27]. The broadband filtered signal is then synthesized from the outputs of subband filters [26].…”

Section: Introductionmentioning

confidence: 99%

“…The observed signal statistics estimation errors and reverberation can be regarded as steering vector estimation errors, which makes the beamformers also robust against statistics estimation errors and reverberation. In our previous work [25], we evaluated the performance of the robust beamformers in speech signal processing by taking into account different noise conditions and small amounts of reverberation. The experimental results showed that the robust beamformers are promising in both improving the speech quality and speech intelligibility.…”

Section: Introductionmentioning

confidence: 99%

Experimental study of robust acoustic beamforming for speech acquisition in reverberant and noisy environments

Zhao

Jensen

et al. 2020

Applied Acoustics

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Experimental study of robust beamforming techniques for acoustic applications

Cited by 8 publications

References 24 publications

Robust Constrained MFMVDR Filters for Single-Channel Speech Enhancement Based on Spherical Uncertainty Set

Robust Constrained MFMVDR Filters for Single-Channel Speech Enhancement Based on Spherical Uncertainty Set

Hearing Aid-controlled Beamformer for Binaural Speech Enhancement Using a Model-based Approach

Experimental study of robust acoustic beamforming for speech acquisition in reverberant and noisy environments

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