Paper Info
Title
Joint maximum likelihood estimation of late reverberant and speech 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. Since the anechoic speech PSD is usually unknown in advance, it is estimated as well. As a closed-form solution for the maximum likelihood estimator 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 outperforms competing estimators. Moreover, when used in a multi-microphone dereverberation and noise reduction algorithm, the best performance in terms of the log-spectral distance is achieved when employing the proposed PSD estimator.
Year
DOI
Venue
2016
10.1109/ICASSP.2016.7471655
ICASSP
Field
DocType
ISSN
Reverberation,Pattern recognition,Computer science,Maximum likelihood,Spectral density,Anechoic chamber,Artificial intelligence,Estimation theory,Maximum likelihood sequence estimation,Estimator,Newton's method
Conference
1520-6149
Citations 
PageRank 
References 
4
0.47
6
Authors
3
Name
Order
Citations
PageRank
Ofer Schwartz1646.76
Sharon Gannot21754130.51
Emanuel A. P. Habets360466.23