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Emanuel A.p. Habets - One of the best experts on this subject based on the ideXlab platform.
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evaluation and comparison of late Reverberation power spectral density estimators
IEEE Transactions on Audio Speech and Language Processing, 2018Co-Authors: Sebastian Braun, Emanuel A.p. Habets, Ofer Schwartz, Sharon Gannot, Adam Kuklasinski, Oliver Thiergart, Simon Doclo, Jesper JensenAbstract:Reduction of late Reverberation can be achieved using spatio-spectral filters, such as the multichannel Wiener filter. To compute this filter, an estimate of the late Reverberation power spectral density (PSD) is required. In recent years, a multitude of late Reverberation PSD estimators have been proposed. In this paper, these estimators are categorized into several classes, their relations and differences are discussed, and a comprehensive experimental comparison is provided. To compare their performance, simulations in controlled as well as practical scenarios are conducted. It is shown that a common weakness of spatial coherence-based estimators is their performance in high direct-to-diffuse ratio conditions. To mitigate this problem, a correction method is proposed and evaluated. It is shown that the proposed correction method can decrease the speech distortion without significantly affecting the Reverberation reduction.
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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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maximum likelihood estimation of the late reverberant power spectral density in noisy environments
Workshop on Applications of Signal Processing to Audio and Acoustics, 2015Co-Authors: Ofer Schwartz, Sharon Gannot, Sebastian Braun, Emanuel A.p. HabetsAbstract: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.
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Blind estimation of Reverberation time based on the distribution of signal decay rates
2008 IEEE International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: Emanuel A.p. Habets, Patrick A. NaylorAbstract:The Reverberation time is one of the most prominent acoustic characteristics of an enclosure. Its value can be used to predict speech intelligibility, and is used by speech enhancement techniques to suppress Reverberation. The Reverberation time is usually obtained by analysing the decay rate of (i) the energy decay curve that is observed when a noise source is switched off, and (ii) the energy decay curve of the room impulse response. Estimating the Reverberation time using only the observed reverberant speech signal, i.e., blind estimation, is required for speech evaluation and enhancement techniques. Recently, (semi) blind methods have been developed. Unfortunately, these methods are not very accurate when the source consists of a human speaker, and unnatural speech pauses are required to detect and/or track the decay. In this paper we extract and analyse the decay rate of the energy envelope blindly from the observed Reverberation speech signal in the short-time Fourier transform domain. We develop a method to estimate the Reverberation time using a property of the distribution of the decay rates. Experimental results using simulated and real reverberant speech signals demonstrate the performance of the new method.
Ofer Schwartz - One of the best experts on this subject based on the ideXlab platform.
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evaluation and comparison of late Reverberation power spectral density estimators
IEEE Transactions on Audio Speech and Language Processing, 2018Co-Authors: Sebastian Braun, Emanuel A.p. Habets, Ofer Schwartz, Sharon Gannot, Adam Kuklasinski, Oliver Thiergart, Simon Doclo, Jesper JensenAbstract:Reduction of late Reverberation can be achieved using spatio-spectral filters, such as the multichannel Wiener filter. To compute this filter, an estimate of the late Reverberation power spectral density (PSD) is required. In recent years, a multitude of late Reverberation PSD estimators have been proposed. In this paper, these estimators are categorized into several classes, their relations and differences are discussed, and a comprehensive experimental comparison is provided. To compare their performance, simulations in controlled as well as practical scenarios are conducted. It is shown that a common weakness of spatial coherence-based estimators is their performance in high direct-to-diffuse ratio conditions. To mitigate this problem, a correction method is proposed and evaluated. It is shown that the proposed correction method can decrease the speech distortion without significantly affecting the Reverberation reduction.
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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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maximum likelihood estimation of the late reverberant power spectral density in noisy environments
Workshop on Applications of Signal Processing to Audio and Acoustics, 2015Co-Authors: Ofer Schwartz, Sharon Gannot, Sebastian Braun, Emanuel A.p. HabetsAbstract: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.
Jesper Jensen - One of the best experts on this subject based on the ideXlab platform.
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evaluation and comparison of late Reverberation power spectral density estimators
IEEE Transactions on Audio Speech and Language Processing, 2018Co-Authors: Sebastian Braun, Emanuel A.p. Habets, Ofer Schwartz, Sharon Gannot, Adam Kuklasinski, Oliver Thiergart, Simon Doclo, Jesper JensenAbstract:Reduction of late Reverberation can be achieved using spatio-spectral filters, such as the multichannel Wiener filter. To compute this filter, an estimate of the late Reverberation power spectral density (PSD) is required. In recent years, a multitude of late Reverberation PSD estimators have been proposed. In this paper, these estimators are categorized into several classes, their relations and differences are discussed, and a comprehensive experimental comparison is provided. To compare their performance, simulations in controlled as well as practical scenarios are conducted. It is shown that a common weakness of spatial coherence-based estimators is their performance in high direct-to-diffuse ratio conditions. To mitigate this problem, a correction method is proposed and evaluated. It is shown that the proposed correction method can decrease the speech distortion without significantly affecting the Reverberation reduction.
Simon Doclo - One of the best experts on this subject based on the ideXlab platform.
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evaluation and comparison of late Reverberation power spectral density estimators
IEEE Transactions on Audio Speech and Language Processing, 2018Co-Authors: Sebastian Braun, Emanuel A.p. Habets, Ofer Schwartz, Sharon Gannot, Adam Kuklasinski, Oliver Thiergart, Simon Doclo, Jesper JensenAbstract:Reduction of late Reverberation can be achieved using spatio-spectral filters, such as the multichannel Wiener filter. To compute this filter, an estimate of the late Reverberation power spectral density (PSD) is required. In recent years, a multitude of late Reverberation PSD estimators have been proposed. In this paper, these estimators are categorized into several classes, their relations and differences are discussed, and a comprehensive experimental comparison is provided. To compare their performance, simulations in controlled as well as practical scenarios are conducted. It is shown that a common weakness of spatial coherence-based estimators is their performance in high direct-to-diffuse ratio conditions. To mitigate this problem, a correction method is proposed and evaluated. It is shown that the proposed correction method can decrease the speech distortion without significantly affecting the Reverberation reduction.
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joint late Reverberation and noise power spectral density estimation in a spatially homogeneous noise field
International Conference on Acoustics Speech and Signal Processing, 2018Co-Authors: Ina Kodrasi, Simon DocloAbstract:Many multi-channel deReverberation and noise reduction techniques such as the multi-channel Wiener filter (MWF) require an estimate of the late Reverberation and noise power spectral densities (PSDs). State-of-the-art multi-channel methods for estimating the late Reverberation PSD typically assume that the noise PSD matrix is known. Instead of assuming that the noise PSD matrix is known, in this paper we model the noise as a spatially homogeneous sound field with an unknown time-varying PSD and a known time-invariant spatial coherence matrix. Based on this model, two joint estimators of the late Reverberation and noise PSDs are proposed, i.e., a non-blocking-based estimator which simultaneously estimates the target signal, late Reverberation, and noise PSDs, and a blocking-based estimator which first estimates the late Reverberation and noise PSDs at the output of a blocking matrix aiming to block the target signal. Experimental results show that the proposed blocking-based estimator yields the best performance when used in an MWF, even resulting in a similar or better performance than a state-of-the-art blocking-based estimator of the late Reverberation PSD which assumes that the noise PSD matrix is known.
Sharon Gannot - One of the best experts on this subject based on the ideXlab platform.
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evaluation and comparison of late Reverberation power spectral density estimators
IEEE Transactions on Audio Speech and Language Processing, 2018Co-Authors: Sebastian Braun, Emanuel A.p. Habets, Ofer Schwartz, Sharon Gannot, Adam Kuklasinski, Oliver Thiergart, Simon Doclo, Jesper JensenAbstract:Reduction of late Reverberation can be achieved using spatio-spectral filters, such as the multichannel Wiener filter. To compute this filter, an estimate of the late Reverberation power spectral density (PSD) is required. In recent years, a multitude of late Reverberation PSD estimators have been proposed. In this paper, these estimators are categorized into several classes, their relations and differences are discussed, and a comprehensive experimental comparison is provided. To compare their performance, simulations in controlled as well as practical scenarios are conducted. It is shown that a common weakness of spatial coherence-based estimators is their performance in high direct-to-diffuse ratio conditions. To mitigate this problem, a correction method is proposed and evaluated. It is shown that the proposed correction method can decrease the speech distortion without significantly affecting the Reverberation reduction.
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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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maximum likelihood estimation of the late reverberant power spectral density in noisy environments
Workshop on Applications of Signal Processing to Audio and Acoustics, 2015Co-Authors: Ofer Schwartz, Sharon Gannot, Sebastian Braun, Emanuel A.p. HabetsAbstract: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.
