Robust Joint Estimation of Multimicrophone Signal Model Parameters

Koutrouvelis, Andreas I.; Hendriks, Richard C.; Heusdens, Richard; Jensen, Jesper

doi:10.1109/taslp.2019.2911167

Cited by 20 publications

(44 citation statements)

References 57 publications

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“…2. We use a uniform linear array (ULA) consisting of M = 8 microphones and design the rank-r optimal beamformer given in (26) for noise reduction. In this case, one can choose any microphone as the reference.…”

Section: Rank-1 Beamformer Without Near-end Noisementioning

confidence: 99%

A Study on Reference Microphone Selection for Multi-Microphone Speech Enhancement

Zhang

Chen

Dai

et al. 2021

IEEE/ACM Trans. Audio Speech Lang. Process.

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Multi-microphone speech enhancement methods typically require a reference position with respect to which the target signal is estimated. Often, this reference position is arbitrarily chosen as one of the reference microphones. However, it has been shown that the choice of the reference microphone can have a significant impact on the final noise reduction performance. In this paper, we therefore theoretically analyze the impact of selecting a reference on the noise reduction performance with near-end noise being taken into account. Following the generalized eigenvalue decomposition (GEVD) based optimal variable span filtering framework, we find that for any linear beamformer, the output signal-to-noise ratio (SNR) taking both the near-end and far-end noise into account is reference dependent. Only when the near-end noise is neglected, the output SNR of rank-1 beamformers does not depend on the reference position. However, in general for rank-r beamformers with r > 1 (e.g., the multichannel Wiener filter) the performance does depend on the reference position. Based on these, we propose an optimal algorithm for microphone reference selection that maximizes the output SNR. In addition, we propose a lower-complexity algorithm that is still optimal for rank-1 beamformers, but suboptimal for the general r > 1 rank beamformers. Experiments using a simulated microphone array validate the effectiveness of both proposed methods and show that in terms of quality, several dB can be gained by selecting the proper reference microphone.

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Section: Rank-1 Beamformer Without Near-end Noisementioning

confidence: 99%

A Study on Reference Microphone Selection for Multi-Microphone Speech Enhancement

Zhang

Chen

Dai

et al. 2021

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…Note that with (14)- (15), one may easily include further diffuse components, e.g., babble noise, without formally changing the signal model. However, since in this paper, we are mainly concerned with the estimation of the early PSDs ϕ s (l) and the recursive updating of the estimate of the RETFs H(l), we restrict the discussion and simulations, cf.…”

Section: Signal Modelmentioning

confidence: 99%

“…Alternatively, instead of simple thresholding after solving (17), one may solve the minimization problem subject to the nonnegative inequality constraint ϕ s ≥ 0, as proposed in [15].…”

Section: Early Psd Estimation Based On the Early Correlation Matrixmentioning

confidence: 99%

“…In recent years, a number of multi-microphone approaches to the estimation of early speech PSDs, late reverberant PSDs, and/or noise PSDs have been proposed, which rely on a spatial correlation matrix model in the short-time Fourier transform (STFT) domain [5]- [15]. In order to estimate these PSDs, some parameters of the correlation matrix model are assumed to be known or estimated beforehand, such as the direction(s) of arrival (DoA(s)) or the relative early transfer function(s) (RETF(s)) associated to the source(s) [5]- [13], or the spatial coherence matrix of the noise or the late reverberant component, where in particular the latter is commonly modeled as a spatially diffuse sound field [6]- [8], [10]- [16].…”

Section: Introductionmentioning

confidence: 99%

“…Likewise, one may also jointly estimate several PSDs associated to different kinds of coherence matrices, e.g., one may jointly estimate early speech PSD(s), the late reverberant PSD, and noise PSD(s) [9], [11], [12]. In [15], joint estimation of the RETFs, the early speech PSDs, the late reverberant PSD, and the noise PSDs is proposed using simultaneous confirmatory factor analysis in multiple frames, i.e. by jointly arXiv:1906.07493v1 [eess.AS] 18 Jun 2019 minimizing a number of approximation errors defined over several frames, during which the RETFs are assumed to be stationary.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Square Root-Based Multi-Source Early PSD Estimation and Recursive RETF Update in Reverberant Environments by Means of the Orthogonal Procrustes Problem

Dietzen

Doclo

Moonen

et al. 2020

IEEE/ACM Trans. Audio Speech Lang. Process.

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Multi-channel short-time Fourier transform (STFT) domain-based processing of reverberant microphone signals commonly relies on power-spectral-density (PSD) estimates of early source images, where early refers to reflections contained within the same STFT frame. State-of-the-art approaches to multisource early PSD estimation, given an estimate of the associated relative early transfer functions (RETFs), conventionally minimize the approximation error defined with respect to the early correlation matrix, requiring non-negative inequality constraints on the PSDs. Instead, we here propose to factorize the early correlation matrix and minimize the approximation error defined with respect to the early-correlation-matrix square root. The proposed minimization problem -constituting a generalization of the so-called orthogonal Procrustes problem -seeks a unitary matrix and the square roots of the early PSDs up to an arbitrary complex argument, making non-negative inequality constraints redundant. A solution is obtained iteratively, requiring one singular value decomposition (SVD) per iteration. The estimated unitary matrix and early PSD square roots further allow to recursively update the RETF estimate, which is not inherently possible in the conventional approach. An estimate of the said early-correlation-matrix square root itself is obtained by means of the generalized eigenvalue decomposition (GEVD), where we further propose to restore non-stationarities by desmoothing the generalized eigenvalues in order to compensate for inevitable recursive averaging. Simulation results indicate fast convergence of the proposed multi-source early PSD estimation approach in only one iteration if initialized appropriately, and better performance as compared to the conventional approach.

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