@inproceedings{48ada8b97541456d8b2675506a1857d4,
title = "Maximum likelihood estimation of the late reverberant power spectral density in noisy environments",
abstract = "An estimate of the power spectral density (PSD) of the late reverberation is often required by dereverberation algorithms. In this work, we derive a novel multichannel maximum likelihood (ML) estimator for the PSD of the reverberation that can be applied in noisy environments. The direct path is first blocked by a blocking matrix and the output is considered as the observed data. Then, the ML criterion for estimating the reverberation PSD is stated. As a closed-form solution for the maximum likelihood estimator (MLE) is unavailable, a Newton method for maximizing the ML criterion is derived. Experimental results show that the proposed estimator provides an accurate estimate of the PSD, and is outperforming competing estimators. Moreover, when used in a multi-microphone noise reduction and dereverberation algorithm, the estimated reverberation PSD is shown to provide improved performance measures as compared with the competing estimators.",
keywords = "Closed-form solutions, Maximum likelihood estimation, Microphones, Noise measurement, Reverberation, Speech",
author = "Ofer Schwartz and Sebastian Braun and Sharon Gannot and Habets, {Emanuel A.P.}",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2015 ; Conference date: 18-10-2015 Through 21-10-2015",
year = "2015",
month = nov,
day = "24",
doi = "10.1109/waspaa.2015.7336919",
language = "الإنجليزيّة",
series = "2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2015",
address = "الولايات المتّحدة",
}