The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform

Masaaki Miyakoshi - One of the best experts on this subject based on the ideXlab platform.

  • ICASSP - Joint estimation of signal and Noise Correlation matrices and its application to inverse filtering
    2009 IEEE International Conference on Acoustics Speech and Signal Processing, 2009
    Co-Authors: Akira Tanaka, Masaaki Miyakoshi
    Abstract:

    Noise suppression by linear filters for a time series is discussed. We propose a method for jointly estimating signal and Noise Correlation matrices by incorporating steering vectors of the Noise or eigenvectors of the Noise Correlation Matrix as well as steering vectors of the target signals. Our estimates bring us two significant advantages. One is reduction of computational cost in obtaining the Wiener filter since the Wiener post filter, which is combined to the minimum variance distortionless response filter (MVDRF), is no longer needed with the estimates of signal and Noise Correlation matrices. The other is an improvement of the performance of the MVDRF since we can construct the regularized version of it with an estimate of the Noise Correlation Matrix.

  • d o a estimation with singular Noise Correlation Matrix audio signal processing applications
    International Conference on Acoustics Speech and Signal Processing, 2005
    Co-Authors: Akira Tanaka, Masaaki Miyakoshi
    Abstract:

    In this paper, a new method of direction of arrival (D.O.A.) estimation with environmental Noise, whose spatial Correlation Matrix is singular, is proposed. In D.O.A. estimation, identification of signal and Noise subspaces plays a very important role. The identification process can be achieved by (generalized) eigenvalue decomposition of the spatial Correlation Matrix of observations (with respect to that of Noise), if these spatial Correlation matrices are non-singular. However, these mathematical tools cannot be applied to the problems in which the spatial Correlation matrices are singular. The main idea of this work deeply depends on identification of proper and improper eigenvectors of the spatial Correlation Matrix of Noise with respect to that of observations. The results of computer simulations are also presented to verify the efficacy of the proposed method.

  • ICASSP (3) - D.O.A.. estimation with singular Noise Correlation Matrix [audio signal processing applications]
    Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 1
    Co-Authors: Akira Tanaka, Masaaki Miyakoshi
    Abstract:

    In this paper, a new method of direction of arrival (D.O.A.) estimation with environmental Noise, whose spatial Correlation Matrix is singular, is proposed. In D.O.A. estimation, identification of signal and Noise subspaces plays a very important role. The identification process can be achieved by (generalized) eigenvalue decomposition of the spatial Correlation Matrix of observations (with respect to that of Noise), if these spatial Correlation matrices are non-singular. However, these mathematical tools cannot be applied to the problems in which the spatial Correlation matrices are singular. The main idea of this work deeply depends on identification of proper and improper eigenvectors of the spatial Correlation Matrix of Noise with respect to that of observations. The results of computer simulations are also presented to verify the efficacy of the proposed method.

Akira Tanaka - One of the best experts on this subject based on the ideXlab platform.

  • ICASSP - Joint estimation of signal and Noise Correlation matrices and its application to inverse filtering
    2009 IEEE International Conference on Acoustics Speech and Signal Processing, 2009
    Co-Authors: Akira Tanaka, Masaaki Miyakoshi
    Abstract:

    Noise suppression by linear filters for a time series is discussed. We propose a method for jointly estimating signal and Noise Correlation matrices by incorporating steering vectors of the Noise or eigenvectors of the Noise Correlation Matrix as well as steering vectors of the target signals. Our estimates bring us two significant advantages. One is reduction of computational cost in obtaining the Wiener filter since the Wiener post filter, which is combined to the minimum variance distortionless response filter (MVDRF), is no longer needed with the estimates of signal and Noise Correlation matrices. The other is an improvement of the performance of the MVDRF since we can construct the regularized version of it with an estimate of the Noise Correlation Matrix.

  • d o a estimation with singular Noise Correlation Matrix audio signal processing applications
    International Conference on Acoustics Speech and Signal Processing, 2005
    Co-Authors: Akira Tanaka, Masaaki Miyakoshi
    Abstract:

    In this paper, a new method of direction of arrival (D.O.A.) estimation with environmental Noise, whose spatial Correlation Matrix is singular, is proposed. In D.O.A. estimation, identification of signal and Noise subspaces plays a very important role. The identification process can be achieved by (generalized) eigenvalue decomposition of the spatial Correlation Matrix of observations (with respect to that of Noise), if these spatial Correlation matrices are non-singular. However, these mathematical tools cannot be applied to the problems in which the spatial Correlation matrices are singular. The main idea of this work deeply depends on identification of proper and improper eigenvectors of the spatial Correlation Matrix of Noise with respect to that of observations. The results of computer simulations are also presented to verify the efficacy of the proposed method.

  • ICASSP (3) - D.O.A.. estimation with singular Noise Correlation Matrix [audio signal processing applications]
    Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 1
    Co-Authors: Akira Tanaka, Masaaki Miyakoshi
    Abstract:

    In this paper, a new method of direction of arrival (D.O.A.) estimation with environmental Noise, whose spatial Correlation Matrix is singular, is proposed. In D.O.A. estimation, identification of signal and Noise subspaces plays a very important role. The identification process can be achieved by (generalized) eigenvalue decomposition of the spatial Correlation Matrix of observations (with respect to that of Noise), if these spatial Correlation matrices are non-singular. However, these mathematical tools cannot be applied to the problems in which the spatial Correlation matrices are singular. The main idea of this work deeply depends on identification of proper and improper eigenvectors of the spatial Correlation Matrix of Noise with respect to that of observations. The results of computer simulations are also presented to verify the efficacy of the proposed method.

Timo Gerkmann - One of the best experts on this subject based on the ideXlab platform.

  • Noise Correlation Matrix Estimation for Multi-Microphone Speech Enhancement
    IEEE Transactions on Audio Speech and Language Processing, 2012
    Co-Authors: Richard C. Hendriks, Timo Gerkmann
    Abstract:

    For multi-channel Noise reduction algorithms like the minimum variance distortionless response (MVDR) beamformer, or the multi-channel Wiener filter, an estimate of the Noise Correlation Matrix is needed. For its estimation, it is often proposed in the literature to use a voice activity detector (VAD). However, using a VAD the estimated Matrix can only be updated in speech absence. As a result, during speech presence the Noise Correlation Matrix estimate does not follow changing Noise fields with an appropriate accuracy. This effect is further increased, as in nonstationary Noise voice activity detection is a rather difficult task, and false-alarms are likely to occur. In this paper, we present and analyze an algorithm that estimates the Noise Correlation Matrix without using a VAD. This algorithm is based on measuring the Correlation of the noisy input and a Noise reference which can be obtained, e.g., by steering a null towards the target source. When applied in combination with an MVDR beamformer, it is shown that the proposed Noise Correlation Matrix estimate results in a more accurate beamformer response, a larger signal-to-Noise ratio improvement and a larger instrumentally predicted speech intelligibility when compared to competing algorithms such as the generalized sidelobe canceler, a VAD-based MVDR beamformer, and an MVDR based on the noisy Correlation Matrix.

  • estimation of the Noise Correlation Matrix
    International Conference on Acoustics Speech and Signal Processing, 2011
    Co-Authors: Richard C. Hendriks, Timo Gerkmann
    Abstract:

    To harvest the potential of multi-channel Noise reduction methods, it is crucial to have an accurate estimate of the Noise Correlation Matrix. Existing algorithms either assume speech absence and exploit a voice activity detector (VAD), or make use of additional assumptions like a diffuse Noise field. Therefore, these algorithms are limited with respect to their tracking speed and the type of Noise fields for which they can estimate the Correlation Matrix. In this paper we present a new method for Noise Correlation Matrix estimation that makes no assumptions about the type of Noise field, nor uses a VAD. The presented method exploits the existence of accurate single-channel Noise PSD estimators, as well as the availability of one Noise reference per microphone pair. For spatially and temporally non-stationary Noise fields, the proposed method leads to improved performance compared to widely used state-of-the-art reference methods in terms of both segmental SNR and beamformer response error.

  • ICASSP - Estimation of the Noise Correlation Matrix
    2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011
    Co-Authors: Richard C. Hendriks, Timo Gerkmann
    Abstract:

    To harvest the potential of multi-channel Noise reduction methods, it is crucial to have an accurate estimate of the Noise Correlation Matrix. Existing algorithms either assume speech absence and exploit a voice activity detector (VAD), or make use of additional assumptions like a diffuse Noise field. Therefore, these algorithms are limited with respect to their tracking speed and the type of Noise fields for which they can estimate the Correlation Matrix. In this paper we present a new method for Noise Correlation Matrix estimation that makes no assumptions about the type of Noise field, nor uses a VAD. The presented method exploits the existence of accurate single-channel Noise PSD estimators, as well as the availability of one Noise reference per microphone pair. For spatially and temporally non-stationary Noise fields, the proposed method leads to improved performance compared to widely used state-of-the-art reference methods in terms of both segmental SNR and beamformer response error.

Marc Moonen - One of the best experts on this subject based on the ideXlab platform.

  • GEVD Based Speech and Noise Correlation Matrix Estimation for Multichannel Wiener Filter Based Noise Reduction
    2018 26th European Signal Processing Conference (EUSIPCO), 2018
    Co-Authors: Robbe Van Rompaey, Marc Moonen
    Abstract:

    In a single speech source Noise reduction scenario, the frequency domain Correlation Matrix of the speech signal is often assumed to be a rank-l Matrix. In multichannel Wiener filter (MWF) based Noise reduction, this assumption may be used to define an optimization criterion to estimate the positive definite speech Correlation Matrix together with the Noise Correlation Matrix, from sample `speech+Noise' and `Noise-only' Correlation matrices. The estimated Correlation matrices then define the MWF. In generalized eigenvalue decomposition (GEVD) based MWF, this optimization criterion involves a prewhitening with the sample `Noise-only' Correlation Matrix, which in particular leads to a compact expression for the MWF. However, a more accurate form would include a prewhitening with the estimated Noise Correlation Matrix instead of with the sample `Noise-only' Correlation Matrix. Unfortunately this leads to a more difficult optimization problem, where the prewhitening indeed involves one of the optimization variables. In this paper, it is demonstrated that the modified optimization criterion, remarkably, leads to only minor modifications in the estimated Correlation matrices and eventually the same MWF, which justifies the use of the original optimization criterion as a simpler substitute.

  • EUSIPCO - GEVD Based Speech and Noise Correlation Matrix Estimation for Multichannel Wiener Filter Based Noise Reduction
    2018 26th European Signal Processing Conference (EUSIPCO), 2018
    Co-Authors: Robbe Van Rompaey, Marc Moonen
    Abstract:

    In a single speech source Noise reduction scenario, the frequency domain Correlation Matrix of the speech signal is often assumed to be a rank-l Matrix. In multichannel Wiener filter (MWF) based Noise reduction, this assumption may be used to define an optimization criterion to estimate the positive definite speech Correlation Matrix together with the Noise Correlation Matrix, from sample ‘speech+Noise’ and ‘Noise-only’ Correlation matrices. The estimated Correlation matrices then define the MWF. In generalized eigenvalue decomposition (GEVD) based MWF, this optimization criterion involves a prewhitening with the sample ‘Noise-only’ Correlation Matrix, which in particular leads to a compact expression for the MWF. However, a more accurate form would include a prewhitening with the estimated Noise Correlation Matrix instead of with the sample ‘Noise-only’ Correlation Matrix. Unfortunately this leads to a more difficult optimization problem, where the prewhitening indeed involves one of the optimization variables. In this paper, it is demonstrated that the modified optimization criterion, remarkably, leads to only minor modifications in the estimated Correlation matrices and eventually the same MWF, which justifies the use of the original optimization criterion as a simpler substitute.

  • Robust Adaptive Time Delay Estimation for Speaker Localization in Noisy and Reverberant Acoustic Environments
    EURASIP Journal on Advances in Signal Processing, 2003
    Co-Authors: Simon Doclo, Marc Moonen
    Abstract:

    Two adaptive algorithms are presented for robust time delay estimation (TDE) in acoustic environments with a large amount of background Noise and reverberation. Recently, an adaptive eigenvalue decomposition (EVD) algorithm has been developed for TDE in highly reverberant acoustic environments. In this paper, we extend the adaptive EVD algorithm to noisy and reverberant acoustic environments, by deriving an adaptive stochastic gradient algorithm for the generalized eigenvalue decomposition (GEVD) or by prewhitening the noisy microphone signals. We have performed simulations using a localized and a diffuse Noise source for several SNRs, showing that the time delays can be estimated more accurately using the adaptive GEVD algorithm than using the adaptive EVD algorithm. In addition, we have analyzed the sensitivity of the adaptive GEVD algorithm with respect to the accuracy of the Noise Correlation Matrix estimate, showing that its performance may be quite sensitive, especially for low SNR scenarios.

Richard C. Hendriks - One of the best experts on this subject based on the ideXlab platform.

  • Distributed Rate-Constrained LCMV Beamforming
    IEEE Signal Processing Letters, 2019
    Co-Authors: Jie Zhang, Andreas I. Koutrouvelis, Richard Heusdens, Richard C. Hendriks
    Abstract:

    In this letter, we propose a decentralized framework for rate-distributed linearly constrained minimum variance (LCMV) beamforming in wireless acoustic sensor networks. To save the energy usage within the network, we propose to minimize the transmission cost and put a constraint on the Noise reduction performance. Subsequently, we decentralize the obtained LCMV filter structure by exploiting an imposed block diagonal form of the Noise Correlation Matrix. As a result, the beamformer weights are calculated in a decentralized fashion and each node can determine its quantization rate locally. Finally, numerical results validate the proposed method.

  • Noise Correlation Matrix Estimation for Multi-Microphone Speech Enhancement
    IEEE Transactions on Audio Speech and Language Processing, 2012
    Co-Authors: Richard C. Hendriks, Timo Gerkmann
    Abstract:

    For multi-channel Noise reduction algorithms like the minimum variance distortionless response (MVDR) beamformer, or the multi-channel Wiener filter, an estimate of the Noise Correlation Matrix is needed. For its estimation, it is often proposed in the literature to use a voice activity detector (VAD). However, using a VAD the estimated Matrix can only be updated in speech absence. As a result, during speech presence the Noise Correlation Matrix estimate does not follow changing Noise fields with an appropriate accuracy. This effect is further increased, as in nonstationary Noise voice activity detection is a rather difficult task, and false-alarms are likely to occur. In this paper, we present and analyze an algorithm that estimates the Noise Correlation Matrix without using a VAD. This algorithm is based on measuring the Correlation of the noisy input and a Noise reference which can be obtained, e.g., by steering a null towards the target source. When applied in combination with an MVDR beamformer, it is shown that the proposed Noise Correlation Matrix estimate results in a more accurate beamformer response, a larger signal-to-Noise ratio improvement and a larger instrumentally predicted speech intelligibility when compared to competing algorithms such as the generalized sidelobe canceler, a VAD-based MVDR beamformer, and an MVDR based on the noisy Correlation Matrix.

  • estimation of the Noise Correlation Matrix
    International Conference on Acoustics Speech and Signal Processing, 2011
    Co-Authors: Richard C. Hendriks, Timo Gerkmann
    Abstract:

    To harvest the potential of multi-channel Noise reduction methods, it is crucial to have an accurate estimate of the Noise Correlation Matrix. Existing algorithms either assume speech absence and exploit a voice activity detector (VAD), or make use of additional assumptions like a diffuse Noise field. Therefore, these algorithms are limited with respect to their tracking speed and the type of Noise fields for which they can estimate the Correlation Matrix. In this paper we present a new method for Noise Correlation Matrix estimation that makes no assumptions about the type of Noise field, nor uses a VAD. The presented method exploits the existence of accurate single-channel Noise PSD estimators, as well as the availability of one Noise reference per microphone pair. For spatially and temporally non-stationary Noise fields, the proposed method leads to improved performance compared to widely used state-of-the-art reference methods in terms of both segmental SNR and beamformer response error.

  • ICASSP - Estimation of the Noise Correlation Matrix
    2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011
    Co-Authors: Richard C. Hendriks, Timo Gerkmann
    Abstract:

    To harvest the potential of multi-channel Noise reduction methods, it is crucial to have an accurate estimate of the Noise Correlation Matrix. Existing algorithms either assume speech absence and exploit a voice activity detector (VAD), or make use of additional assumptions like a diffuse Noise field. Therefore, these algorithms are limited with respect to their tracking speed and the type of Noise fields for which they can estimate the Correlation Matrix. In this paper we present a new method for Noise Correlation Matrix estimation that makes no assumptions about the type of Noise field, nor uses a VAD. The presented method exploits the existence of accurate single-channel Noise PSD estimators, as well as the availability of one Noise reference per microphone pair. For spatially and temporally non-stationary Noise fields, the proposed method leads to improved performance compared to widely used state-of-the-art reference methods in terms of both segmental SNR and beamformer response error.