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@inproceedings{faucris.121163944,
abstract = {Dereverberation methods based on coherent-to-diffuse power ratio (CDR)
estimates exploit the spatial properties of signals to suppress late
reverberation components. Naturally, the quality of the CDR estimate has
a major effect on the quality of the dereverberated signals. This paper
presents a statistical study of the performance of state-of-the-art CDR
estimators under a Gaussian signal model. The study reveals that the
variance of the magnitude of the spatial coherence estimate is the major
cause for the degradation of known CDR estimators, while the variance
of the phase has only limited impact. Moreover, the variance has more
impact on direction-of-arrival (DOA)-independent CDR estimators than on
DOA-dependent estimators. Accordingly, we propose a cepstrum
thresholding technique to reduce the variance of the spatial coherence
estimate. Experimental results show that better performance can be
achieved for dereverberation when the proposed spatial coherence method
is applied to CDR estimators, especially for the DOA-independent
estimator},
address = {Xi'an, China},
author = {Zheng, Chengshi and Schwarz, Andreas and Li, X. and Kellermann, Walter},
booktitle = {International Workshop on Acoustic Echo and Noise Control (IWAENC)},
date = {2016-09-13/2016-09-16},
doi = {10.1109/IWAENC.2016.7602931},
faupublication = {yes},
isbn = {978-1-5090-2007-2},
keywords = {Speech dereverberation; statistical analysis; cepstrum thresholding},
pages = {1-5},
peerreviewed = {Yes},
title = {{Statistical} analysis and improvement of coherent-to-diffuse power ratio estimators for dereverberation},
venue = {Xi'an},
year = {2016}
}
@article{faucris.120065704,
abstract = {This paper studies the statistical performance of the multichannel Wiener filter (MWF) when the weights are computed using estimates of the sample covariance matrices of the noisy and the noise signals. It is well known that the optimal weights of the minimum variance distortionless response beamformer are only determined by the noisy sample covariance matrix or the noise sample covariance matrix, while those of the MWF are determined by both of them. Therefore, the difficulty increases dramatically in statistically analyzing the MWF when compared to analyzing the MVDR, where the main reason is that expressing the general joint probability density function (p.d.f.) of the two sample covariance matrices presented a Hitherto unsolved problem, to the best of our knowledge. For a deeper insight into the statistical performance of the MWF, this paper first introduces a bivariate normal distribution to approximately model the joint p.d.f. of the noisy and the noise sample covariance matrices. Each sample covariance matrix is approximately modeled by a random scalar multiplied by its true covariance matrix. This approximation is designed to preserve both the bias and the mean squared error of the matrix with respect to a natural distance on covariance matrices. The correlation of the bivariate normal distribution, referred to as the sample covariance matrices intrinsic correlation coefficient, captures all second-order dependencies of the noisy and the noise sample covariance matrices. By using the proposed bivariate normal distribution, the performance of the MWF can be predicted from the derived analytical expressions and many interesting results are revealed. As an example, the theoretical analysis demonstrates that the MWF performance may degrade in terms of noise reduction and signal-to-noise-ratio improvement when using more sensors in some noise scenario},
author = {Zheng, Chengshi and Deleforge, Antoine and Li, Xiaodong and Kellermann, Walter},
doi = {10.1109/TASLP.2018.2800283},
faupublication = {yes},
journal = {IEEE/ACM Transactions on Audio, Speech and Language Processing},
keywords = {Bivariate normal distribution; multichannel Wiener filter; sample covariance matrix; statistical analysis},
pages = {951-966},
peerreviewed = {Yes},
title = {{Statistical} analysis of the multichannel {Wiener} filter using a bivariate normal distribution for sample covariance matrices},
url = {http://ieeexplore.ieee.org/document/8276308/},
volume = {26},
year = {2018}
}