The Experts below are selected from a list of 55563 Experts worldwide ranked by ideXlab platform

V Ferrero - One of the best experts on this subject based on the ideXlab platform.

Simon Doclo - One of the best experts on this subject based on the ideXlab platform.

  • evaluation and comparison of late reverberation Power Spectral Density estimators
    2018
    Co-Authors: Sebastian Braun, Adam Kuklasinski, Ofer Schwartz, Oliver Thiergart, Emanuel A P Habets, Sharon Gannot, Simon Doclo, Jesper Jensen
    Abstract:

    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.

  • late reverberant Power Spectral Density estimation based on an eigenvalue decomposition
    2017
    Co-Authors: Ina Kodrasi, Simon Doclo
    Abstract:

    Multi-channel methods for estimating the late reverberant Power Spectral Density (PSD) rely on an estimate of the direction of arrival (DOA) of the speech source or of the relative early transfer functions (RETFs) of the target signal from a reference microphone to all microphones. The DOA and the RETFs may be difficult to estimate accurately, particularly in highly reverberant and noisy scenarios. In this paper we propose a novel multi-channel method to estimate the late reverberant PSD which does not require estimates of the DOA or RETFs. The late reverberation is modeled as an isotropic sound field and the late reverberant PSD is estimated based on the eigenvalues of the prewhitened received signal PSD matrix. Experimental results demonstrate the advantages of using the proposed estimator in a multi-channel Wiener filter for speech dereverberation, outperforming a recently proposed maximum likelihood estimator both when the DOA is perfectly estimated as well as in the presence of DOA estimation errors.

  • noise Power Spectral Density estimation using maxnsr blocking matrix
    2015
    Co-Authors: Lin Wang, Timo Gerkmann, Simon Doclo
    Abstract:

    In this paper, a multi-microphone noise reduction system based on the generalized sidelobe canceller (GSC) structure is investigated. The system consists of a fixed beamformer providing an enhanced speech reference, a blocking matrix providing a noise reference by suppressing the target speech, and a single-channel Spectral post-filter. The Spectral post-filter requires the Power Spectral Density (PSD) of the residual noise in the speech reference, which can in principle be estimated from the PSD of the noise reference. However, due to speech leakage in the noise reference, the noise PSD is overestimated, leading to target speech distortion. To minimize the influence of the speech leakage, a maximum noise-to-speech ratio (MaxNSR) blocking matrix is proposed, which maximizes the ratio between the noise and the speech leakage in the noise reference. The proposed blocking matrix can be computed from the generalized eigenvalue decomposition of the correlation matrix of the microphone signals and the noise coherence matrix, which is assumed to be time-invariant. Experimental results in both stationary and nonstationary diffuse noise fields show that the proposed algorithm outperforms existing blocking matrices in terms of target speech blocking ability, noise estimation and noise reduction performance.

R. Martin - One of the best experts on this subject based on the ideXlab platform.

  • an evaluation of noise Power Spectral Density estimation algorithms in adverse acoustic environments
    2011
    Co-Authors: Nasser Mohammadiha, Jalal Taghia, Jinqiu Sang, Vaclav Bouse, R. Martin
    Abstract:

    Noise Power Spectral Density estimation is an important component of speech enhancement systems due to its considerable effect on the quality and the intelligibility of the enhanced speech. Recently, many new algorithms have been proposed and significant progress in noise tracking has been made. In this paper, we present an evaluation framework for measuring the performance of some recently proposed and some well-known noise Power Spectral Density estimators and compare their performance in adverse acoustic environments. In this investigation we do not only consider the performance in the mean of a Spectral distance measure but also evaluate the variance of the estimators as the latter is related to undesirable fluctuations also known as musical noise. By providing a variety of different non-stationary noises, the robustness of noise estimators in adverse environments is examined.

  • bias compensation methods for minimum statistics noise Power Spectral Density estimation
    2006
    Co-Authors: R. Martin
    Abstract:

    The Minimum Statistics noise Power Spectral Density (psd) estimation approach is based on tracking minima of a short term Power Spectral Density (psd) estimate in frequency subbands. Since the short term minimum Power is always smaller than (or in trivial cases equal to) the mean Power, the minimum noise Power estimate is a biased estimate of the mean Power. For an accurate noise Power estimate this bias must be compensated.In this paper we review bias compensation methods for moving average and first-order recursive smoothed psd estimates. While for some cases exact expressions for the bias are available, approximations are required in general. We present approximations which allow an efficient computation and compensation of the bias. We discuss factors that influence the bias and show that the method is to some extent robust to variations of the signal statistics. Besides different smoothing methods, we discuss the effect of overlapping Spectral analysis windows and of signal correlation.

  • Noise Power Spectral Density estimation based on optimal smoothing and minimum statistics
    2001
    Co-Authors: R. Martin
    Abstract:

    We describe a method to estimate the Power Spectral Density of nonstationary noise when a noisy speech signal is given. The method can be combined with any speech enhancement algorithm which requires a noise Power Spectral Density estimate. In contrast to other methods, our approach does not use a voice activity detector. Instead it tracks Spectral minima in each frequency band without any distinction between speech activity and speech pause. By minimizing a conditional mean square estimation error criterion in each time step we derive the optimal smoothing parameter for recursive smoothing of the Power Spectral Density of the noisy speech signal. Based on the optimally smoothed Power Spectral Density estimate and the analysis of the statistics of Spectral minima an unbiased noise estimator is developed. The estimator is well suited for real time implementations. Furthermore, to improve the performance in nonstationary noise we introduce a method to speed up the tracking of the Spectral minima. Finally, we evaluate the proposed method in the context of speech enhancement and low bit rate speech coding with various noise types.

S Camatel - One of the best experts on this subject based on the ideXlab platform.

Emanuel A P Habets - One of the best experts on this subject based on the ideXlab platform.

  • evaluation and comparison of late reverberation Power Spectral Density estimators
    2018
    Co-Authors: Sebastian Braun, Adam Kuklasinski, Ofer Schwartz, Oliver Thiergart, Emanuel A P Habets, Sharon Gannot, Simon Doclo, Jesper Jensen
    Abstract:

    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.

  • joint maximum likelihood estimation of late reverberant and speech Power Spectral Density in noisy environments
    2016
    Co-Authors: Ofer Schwartz, Sharon Gannot, Emanuel A P Habets
    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.

  • maximum likelihood estimation of the late reverberant Power Spectral Density in noisy environments
    2015
    Co-Authors: Ofer Schwartz, Sebastian Braun, Sharon Gannot, Emanuel A P Habets
    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.