The Experts below are selected from a list of 114732 Experts worldwide ranked by ideXlab platform
Sofiene Affes - One of the best experts on this subject based on the ideXlab platform.
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the generalization of narrowband localization methods to broadband environments via parametrization of the spatial Correlation Matrix
2007Co-Authors: Jacek P Dmochowski, Jacob Benesty, Sofiene AffesAbstract:The need to localize a radiating signal source is necessitated in applications ranging from distant talker speech pick-up to automatic video camera steering. The majority of literature detailing source localization is presented in the narrowband signal context; however, acoustic signals are naturally very broadband. As a result, classical narrowband techniques do not apply in the strict sense, and as a result, the broadband localization literature is yet unrefined. This paper details the manner in which classical narrowband localization methods may be generalized to broadband signal environments — specifically, a nonlinear parametrization of the spatial Correlation Matrix allows one to apply steered beamforming, minimum variance, subspace, and linear predictive spectral estimation methods to the broadband localization problem.
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direction of arrival estimation using the parameterized spatial Correlation Matrix
2007Co-Authors: Jacek P Dmochowski, Jacob Benesty, Sofiene AffesAbstract:The estimation of the direction-of-arrival (DOA) of one or more acoustic sources is an area that has generated much interest in recent years, with applications like automatic video camera steering and multiparty stereophonic teleconferencing entering the market. DOA estimation algorithms are hindered by the effects of background noise and reverberation. Methods based on the time-differences-of-arrival (TDOA) are commonly used to determine the azimuth angle of arrival of an acoustic source. TDOA-based methods compute each relative delay using only two microphones, even though additional microphones are usually available. This paper deals with DOA estimation based on spatial spectral estimation, and establishes the parameterized spatial Correlation Matrix as the framework for this class of DOA estimators. This Matrix jointly takes into account all pairs of microphones, and is at the heart of several broadband spatial spectral estimators, including steered-response power (SRP) algorithms. This paper reviews and evaluates these broadband spatial spectral estimators, comparing their performance to TDOA-based locators. In addition, an eigenanalysis of the parameterized spatial Correlation Matrix is performed and reveals that such analysis allows one to estimate the channel attenuation from factors such as uncalibrated microphones. This estimate generalizes the broadband minimum variance spatial spectral estimator to more general signal models. A DOA estimator based on the multichannel cross Correlation coefficient (MCCC) is also proposed. The performance of all proposed algorithms is included in the evaluation. It is shown that adding extra microphones helps combat the effects of background noise and reverberation. Furthermore, the link between accurate spatial spectral estimation and corresponding DOA estimation is investigated. The application of the minimum variance and MCCC methods to the spatial spectral estimation problem leads to better resolution than that of the commonly used fixed-weighted SRP spectrum. However, this increased spatial spectral resolution does not always translate to more accurate DOA estimation
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direction of arrival estimation using eigenanalysis of the parameterized spatial Correlation Matrix
2007Co-Authors: Jacek P Dmochowski, Jacob Benesty, Sofiene AffesAbstract:The estimation of the direction-of-arrival (DOA) of one or more acoustic sources is an area that has generated much interest in recent years, with applications like automatic video camera steering and multi-party stereophonic teleconferencing entering the market. Time-difference-of-arrival (TDOA) based methods compute each relative delay using only two microphones, even though additional microphones are usually available, and thus suffer from the effects of background noise and reverberation. This paper deals with DOA estimation based on spatial spectral estimation, and proposes a novel DOA estimator based on the eigenvalues of the parameterized spatial Correlation Matrix. Simulation results confirm the ability of the proposed method to provide reliable estimates even in heavily reverberant environments.
Jacek P Dmochowski - One of the best experts on this subject based on the ideXlab platform.
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the generalization of narrowband localization methods to broadband environments via parametrization of the spatial Correlation Matrix
2007Co-Authors: Jacek P Dmochowski, Jacob Benesty, Sofiene AffesAbstract:The need to localize a radiating signal source is necessitated in applications ranging from distant talker speech pick-up to automatic video camera steering. The majority of literature detailing source localization is presented in the narrowband signal context; however, acoustic signals are naturally very broadband. As a result, classical narrowband techniques do not apply in the strict sense, and as a result, the broadband localization literature is yet unrefined. This paper details the manner in which classical narrowband localization methods may be generalized to broadband signal environments — specifically, a nonlinear parametrization of the spatial Correlation Matrix allows one to apply steered beamforming, minimum variance, subspace, and linear predictive spectral estimation methods to the broadband localization problem.
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direction of arrival estimation using the parameterized spatial Correlation Matrix
2007Co-Authors: Jacek P Dmochowski, Jacob Benesty, Sofiene AffesAbstract:The estimation of the direction-of-arrival (DOA) of one or more acoustic sources is an area that has generated much interest in recent years, with applications like automatic video camera steering and multiparty stereophonic teleconferencing entering the market. DOA estimation algorithms are hindered by the effects of background noise and reverberation. Methods based on the time-differences-of-arrival (TDOA) are commonly used to determine the azimuth angle of arrival of an acoustic source. TDOA-based methods compute each relative delay using only two microphones, even though additional microphones are usually available. This paper deals with DOA estimation based on spatial spectral estimation, and establishes the parameterized spatial Correlation Matrix as the framework for this class of DOA estimators. This Matrix jointly takes into account all pairs of microphones, and is at the heart of several broadband spatial spectral estimators, including steered-response power (SRP) algorithms. This paper reviews and evaluates these broadband spatial spectral estimators, comparing their performance to TDOA-based locators. In addition, an eigenanalysis of the parameterized spatial Correlation Matrix is performed and reveals that such analysis allows one to estimate the channel attenuation from factors such as uncalibrated microphones. This estimate generalizes the broadband minimum variance spatial spectral estimator to more general signal models. A DOA estimator based on the multichannel cross Correlation coefficient (MCCC) is also proposed. The performance of all proposed algorithms is included in the evaluation. It is shown that adding extra microphones helps combat the effects of background noise and reverberation. Furthermore, the link between accurate spatial spectral estimation and corresponding DOA estimation is investigated. The application of the minimum variance and MCCC methods to the spatial spectral estimation problem leads to better resolution than that of the commonly used fixed-weighted SRP spectrum. However, this increased spatial spectral resolution does not always translate to more accurate DOA estimation
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direction of arrival estimation using eigenanalysis of the parameterized spatial Correlation Matrix
2007Co-Authors: Jacek P Dmochowski, Jacob Benesty, Sofiene AffesAbstract:The estimation of the direction-of-arrival (DOA) of one or more acoustic sources is an area that has generated much interest in recent years, with applications like automatic video camera steering and multi-party stereophonic teleconferencing entering the market. Time-difference-of-arrival (TDOA) based methods compute each relative delay using only two microphones, even though additional microphones are usually available, and thus suffer from the effects of background noise and reverberation. This paper deals with DOA estimation based on spatial spectral estimation, and proposes a novel DOA estimator based on the eigenvalues of the parameterized spatial Correlation Matrix. Simulation results confirm the ability of the proposed method to provide reliable estimates even in heavily reverberant environments.
Wenju Liu - One of the best experts on this subject based on the ideXlab platform.
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doa estimation of speech source in noisy environments with weighted spatial bispectrum Correlation Matrix
2014Co-Authors: Wei Xue, Shan Liang, Wenju LiuAbstract:Although the high order statistics (HOS) has promising property against the Gaussian noise, there still lack effective ways to apply the HOS to DOA estimation of the speech source. In this paper, we propose a novel HOS based DOA estimation method for speech source in strong noise conditions. A “weighted spatial bispectrum Correlation Matrix (WSBCM)” is formulated, which contains the spatial Correlation information of bispectrum phase differences. We then propose a new DOA estimator based on the eigenvalue analysis of the WSBCM. Besides the theoretical advantage of the bispectrum against Gaussian noises, the redundant information in the bispectrum domain is also exploited to make the WSBCM noise robust. The WSBCM enables bispectrum weighting to select the speech units in the bispectrum, which further helps to improve the performance. Experimental results demonstrate that the proposed method outperforms existing algorithms in different kinds of noisy environments.
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weighted spatial bispectrum Correlation Matrix for doa estimation in the presence of interferences
2014Co-Authors: Wei Xue, Shan Liang, Wenju LiuAbstract:Besides the undirected environmental noise, the surrounding interference also brings great challenges to the robust DOA estimation of the speech source. As conventional DOA estimation methods always assume an undirected noise model, they usually cannot perform reliably when the strong inference exists. In this paper, we propose a novel interference robust DOA estimation method, which is based on the “weighted spatial bispectrum Correlation Matrix (WSBCM)”. The WSBCM contains the spatial Correlation information of bispectrum phase difference (BPD), and a new DOA estimator is further derived based on the eigenvalue analysis of the WSBCM. By formulating with WSBCM, the proposed method benefits from the redundant DOA-related information provided by the BPD. In addition, the WSBCM enables bispectrum weighting to highlight the pure speech units in the bispectrum, which further helps to improve the performance. In order to compute the bispectrum weights, a decision-directed approach is derived. The effectiveness of the proposed method is demonstrated by experiments conducted under various kinds of interference-existing scenarios.
Timo Gerkmann - One of the best experts on this subject based on the ideXlab platform.
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Noise Correlation Matrix Estimation for Multi-Microphone Speech Enhancement
2012Co-Authors: Richard C. Hendriks, Timo GerkmannAbstract: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.
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estimation of the noise Correlation Matrix
2011Co-Authors: Richard C. Hendriks, Timo GerkmannAbstract: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.
Jacob Benesty - One of the best experts on this subject based on the ideXlab platform.
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the generalization of narrowband localization methods to broadband environments via parametrization of the spatial Correlation Matrix
2007Co-Authors: Jacek P Dmochowski, Jacob Benesty, Sofiene AffesAbstract:The need to localize a radiating signal source is necessitated in applications ranging from distant talker speech pick-up to automatic video camera steering. The majority of literature detailing source localization is presented in the narrowband signal context; however, acoustic signals are naturally very broadband. As a result, classical narrowband techniques do not apply in the strict sense, and as a result, the broadband localization literature is yet unrefined. This paper details the manner in which classical narrowband localization methods may be generalized to broadband signal environments — specifically, a nonlinear parametrization of the spatial Correlation Matrix allows one to apply steered beamforming, minimum variance, subspace, and linear predictive spectral estimation methods to the broadband localization problem.
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direction of arrival estimation using the parameterized spatial Correlation Matrix
2007Co-Authors: Jacek P Dmochowski, Jacob Benesty, Sofiene AffesAbstract:The estimation of the direction-of-arrival (DOA) of one or more acoustic sources is an area that has generated much interest in recent years, with applications like automatic video camera steering and multiparty stereophonic teleconferencing entering the market. DOA estimation algorithms are hindered by the effects of background noise and reverberation. Methods based on the time-differences-of-arrival (TDOA) are commonly used to determine the azimuth angle of arrival of an acoustic source. TDOA-based methods compute each relative delay using only two microphones, even though additional microphones are usually available. This paper deals with DOA estimation based on spatial spectral estimation, and establishes the parameterized spatial Correlation Matrix as the framework for this class of DOA estimators. This Matrix jointly takes into account all pairs of microphones, and is at the heart of several broadband spatial spectral estimators, including steered-response power (SRP) algorithms. This paper reviews and evaluates these broadband spatial spectral estimators, comparing their performance to TDOA-based locators. In addition, an eigenanalysis of the parameterized spatial Correlation Matrix is performed and reveals that such analysis allows one to estimate the channel attenuation from factors such as uncalibrated microphones. This estimate generalizes the broadband minimum variance spatial spectral estimator to more general signal models. A DOA estimator based on the multichannel cross Correlation coefficient (MCCC) is also proposed. The performance of all proposed algorithms is included in the evaluation. It is shown that adding extra microphones helps combat the effects of background noise and reverberation. Furthermore, the link between accurate spatial spectral estimation and corresponding DOA estimation is investigated. The application of the minimum variance and MCCC methods to the spatial spectral estimation problem leads to better resolution than that of the commonly used fixed-weighted SRP spectrum. However, this increased spatial spectral resolution does not always translate to more accurate DOA estimation
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direction of arrival estimation using eigenanalysis of the parameterized spatial Correlation Matrix
2007Co-Authors: Jacek P Dmochowski, Jacob Benesty, Sofiene AffesAbstract:The estimation of the direction-of-arrival (DOA) of one or more acoustic sources is an area that has generated much interest in recent years, with applications like automatic video camera steering and multi-party stereophonic teleconferencing entering the market. Time-difference-of-arrival (TDOA) based methods compute each relative delay using only two microphones, even though additional microphones are usually available, and thus suffer from the effects of background noise and reverberation. This paper deals with DOA estimation based on spatial spectral estimation, and proposes a novel DOA estimator based on the eigenvalues of the parameterized spatial Correlation Matrix. Simulation results confirm the ability of the proposed method to provide reliable estimates even in heavily reverberant environments.
