The Experts below are selected from a list of 8163 Experts worldwide ranked by ideXlab platform
Emanuel A P Habets - One of the best experts on this subject based on the ideXlab platform.
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joint estimation of late reverberant and speech power spectral densities in noisy environments using Frobenius Norm
European Signal Processing Conference, 2016Co-Authors: Ofer Schwartz, Sharon Gannot, Emanuel A P HabetsAbstract:Various dereverberation and noise reduction algorithms require power spectral density estimates of the anechoic speech, reverberation, and noise. In this work, we derive a novel multichannel estimator for the power spectral densities (PSDs) of the reverberation and the speech suitable also for noisy environments. The speech and reverberation PSDs are estimated from all the entries of the received signals power spectral density (PSD) matrix. The Frobenius Norm of a general error matrix is minimized to find the best fitting PSDs. Experimental results show that the proposed estimator provides accurate estimates of the PSDs, and is outperforming competing estimators. Moreover, when used in a multi-microphone noise reduction and dereverberation algorithm, the estimated reverberation and speech PSDs are shown to provide improved performance measures as compared with the competing estimators.
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EUSIPCO - Joint estimation of late reverberant and speech power spectral densities in noisy environments using Frobenius Norm
2016 24th European Signal Processing Conference (EUSIPCO), 2016Co-Authors: Ofer Schwartz, Sharon Gannot, Emanuel A P HabetsAbstract:Various dereverberation and noise reduction algorithms require power spectral density estimates of the anechoic speech, reverberation, and noise. In this work, we derive a novel multichannel estimator for the power spectral densities (PSDs) of the reverberation and the speech suitable also for noisy environments. The speech and reverberation PSDs are estimated from all the entries of the received signals power spectral density (PSD) matrix. The Frobenius Norm of a general error matrix is minimized to find the best fitting PSDs. Experimental results show that the proposed estimator provides accurate estimates of the PSDs, and is outperforming competing estimators. Moreover, when used in a multi-microphone noise reduction and dereverberation algorithm, the estimated reverberation and speech PSDs are shown to provide improved performance measures as compared with the competing estimators.
Peng Shi - One of the best experts on this subject based on the ideXlab platform.
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h control for discrete time linear systems with Frobenius Norm bounded uncertainties
Automatica, 1999Co-Authors: El-kébir Boukas, Peng ShiAbstract:In this paper, we consider the problems of robust stability and control for the class of uncertain discrete-time linear systems with Frobenius Norm-bounded parameter uncertainties in all matrices of the system and output equations. Necessary and sufficient conditions for the above problems are proposed. A linear static state feedback control law is designed, which is in terms of a Riccati inequality. The results obtained here show that the robust control problem of the uncertain system is equivalent to the control problem for a corresponding uncertainty-free system.
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H ∞ control for discrete-time linear systems with Frobenius Norm-bounded uncertainties
Automatica, 1999Co-Authors: El-kébir Boukas, Peng ShiAbstract:In this paper, we consider the problems of robust stability and control for the class of uncertain discrete-time linear systems with Frobenius Norm-bounded parameter uncertainties in all matrices of the system and output equations. Necessary and sufficient conditions for the above problems are proposed. A linear static state feedback control law is designed, which is in terms of a Riccati inequality. The results obtained here show that the robust control problem of the uncertain system is equivalent to the control problem for a corresponding uncertainty-free system.
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H/sub /spl infin// control for discrete-time linear systems with Frobenius Norm-bounded uncertainties
Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No.98CH36207), 1998Co-Authors: El-kébir Boukas, Peng ShiAbstract:In this paper, we consider the problems of robust stability and control for the class of uncertain discrete-time linear systems with Frobenius Norm-bounded parameter uncertainties in all matrices of the system and output equations. Necessary and sufficient conditions for the above problems are proposed. A linear static state feedback control law is designed, which is in terms of a Riccati inequality.
Ofer Schwartz - One of the best experts on this subject based on the ideXlab platform.
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joint estimation of late reverberant and speech power spectral densities in noisy environments using Frobenius Norm
European Signal Processing Conference, 2016Co-Authors: Ofer Schwartz, Sharon Gannot, Emanuel A P HabetsAbstract:Various dereverberation and noise reduction algorithms require power spectral density estimates of the anechoic speech, reverberation, and noise. In this work, we derive a novel multichannel estimator for the power spectral densities (PSDs) of the reverberation and the speech suitable also for noisy environments. The speech and reverberation PSDs are estimated from all the entries of the received signals power spectral density (PSD) matrix. The Frobenius Norm of a general error matrix is minimized to find the best fitting PSDs. Experimental results show that the proposed estimator provides accurate estimates of the PSDs, and is outperforming competing estimators. Moreover, when used in a multi-microphone noise reduction and dereverberation algorithm, the estimated reverberation and speech PSDs are shown to provide improved performance measures as compared with the competing estimators.
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EUSIPCO - Joint estimation of late reverberant and speech power spectral densities in noisy environments using Frobenius Norm
2016 24th European Signal Processing Conference (EUSIPCO), 2016Co-Authors: Ofer Schwartz, Sharon Gannot, Emanuel A P HabetsAbstract:Various dereverberation and noise reduction algorithms require power spectral density estimates of the anechoic speech, reverberation, and noise. In this work, we derive a novel multichannel estimator for the power spectral densities (PSDs) of the reverberation and the speech suitable also for noisy environments. The speech and reverberation PSDs are estimated from all the entries of the received signals power spectral density (PSD) matrix. The Frobenius Norm of a general error matrix is minimized to find the best fitting PSDs. Experimental results show that the proposed estimator provides accurate estimates of the PSDs, and is outperforming competing estimators. Moreover, when used in a multi-microphone noise reduction and dereverberation algorithm, the estimated reverberation and speech PSDs are shown to provide improved performance measures as compared with the competing estimators.
El-kébir Boukas - One of the best experts on this subject based on the ideXlab platform.
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h control for discrete time linear systems with Frobenius Norm bounded uncertainties
Automatica, 1999Co-Authors: El-kébir Boukas, Peng ShiAbstract:In this paper, we consider the problems of robust stability and control for the class of uncertain discrete-time linear systems with Frobenius Norm-bounded parameter uncertainties in all matrices of the system and output equations. Necessary and sufficient conditions for the above problems are proposed. A linear static state feedback control law is designed, which is in terms of a Riccati inequality. The results obtained here show that the robust control problem of the uncertain system is equivalent to the control problem for a corresponding uncertainty-free system.
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H ∞ control for discrete-time linear systems with Frobenius Norm-bounded uncertainties
Automatica, 1999Co-Authors: El-kébir Boukas, Peng ShiAbstract:In this paper, we consider the problems of robust stability and control for the class of uncertain discrete-time linear systems with Frobenius Norm-bounded parameter uncertainties in all matrices of the system and output equations. Necessary and sufficient conditions for the above problems are proposed. A linear static state feedback control law is designed, which is in terms of a Riccati inequality. The results obtained here show that the robust control problem of the uncertain system is equivalent to the control problem for a corresponding uncertainty-free system.
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H/sub /spl infin// control for discrete-time linear systems with Frobenius Norm-bounded uncertainties
Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No.98CH36207), 1998Co-Authors: El-kébir Boukas, Peng ShiAbstract:In this paper, we consider the problems of robust stability and control for the class of uncertain discrete-time linear systems with Frobenius Norm-bounded parameter uncertainties in all matrices of the system and output equations. Necessary and sufficient conditions for the above problems are proposed. A linear static state feedback control law is designed, which is in terms of a Riccati inequality.
Sharon Gannot - One of the best experts on this subject based on the ideXlab platform.
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joint estimation of late reverberant and speech power spectral densities in noisy environments using Frobenius Norm
European Signal Processing Conference, 2016Co-Authors: Ofer Schwartz, Sharon Gannot, Emanuel A P HabetsAbstract:Various dereverberation and noise reduction algorithms require power spectral density estimates of the anechoic speech, reverberation, and noise. In this work, we derive a novel multichannel estimator for the power spectral densities (PSDs) of the reverberation and the speech suitable also for noisy environments. The speech and reverberation PSDs are estimated from all the entries of the received signals power spectral density (PSD) matrix. The Frobenius Norm of a general error matrix is minimized to find the best fitting PSDs. Experimental results show that the proposed estimator provides accurate estimates of the PSDs, and is outperforming competing estimators. Moreover, when used in a multi-microphone noise reduction and dereverberation algorithm, the estimated reverberation and speech PSDs are shown to provide improved performance measures as compared with the competing estimators.
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EUSIPCO - Joint estimation of late reverberant and speech power spectral densities in noisy environments using Frobenius Norm
2016 24th European Signal Processing Conference (EUSIPCO), 2016Co-Authors: Ofer Schwartz, Sharon Gannot, Emanuel A P HabetsAbstract:Various dereverberation and noise reduction algorithms require power spectral density estimates of the anechoic speech, reverberation, and noise. In this work, we derive a novel multichannel estimator for the power spectral densities (PSDs) of the reverberation and the speech suitable also for noisy environments. The speech and reverberation PSDs are estimated from all the entries of the received signals power spectral density (PSD) matrix. The Frobenius Norm of a general error matrix is minimized to find the best fitting PSDs. Experimental results show that the proposed estimator provides accurate estimates of the PSDs, and is outperforming competing estimators. Moreover, when used in a multi-microphone noise reduction and dereverberation algorithm, the estimated reverberation and speech PSDs are shown to provide improved performance measures as compared with the competing estimators.
