The Experts below are selected from a list of 5763 Experts worldwide ranked by ideXlab platform
P.c. Ching - One of the best experts on this subject based on the ideXlab platform.
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adaptive time delay estimation in Nonstationary Signal and or noise power environments
IEEE Transactions on Signal Processing, 1993Co-Authors: K.c. Ho, Y.t. Chan, P.c. ChingAbstract:A model for an adaptive time-delay estimator is proposed to improve its performance in estimating the difference in arrival time of a bandlimited random Signal received by two spatially separated sensors in an environment where the Signal and noise power are time varying. The system comprises two adaptive units: a filter to compensate time shift between the two receiver channels and a gain control to provide Wiener filtering. Both the filter coefficients and the variable gain are adjusted simultaneously by using modifications from the stochastic mean-square-error gradient in the traditional adaptive least-mean-square time-delay estimation (LMSTDE) method. The convergence characteristics of the proposed system are analyzed in detail and compared with those obtained by the traditional technique. Theoretical results show that, unlike the LMSTDE configuration, this arrangement can decouple the adaptation of time shift from the changing Signal and/or noise power, which in turn gives rise to better convergence behavior of the delay estimate. Simulation results are included to illustrate the effectiveness of the new model and corroborate the theoretical developments. >
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Adaptive time-delay estimation in Nonstationary Signal and/or noise power environments
IEEE Transactions on Signal Processing, 1993Co-Authors: K.c. Ho, Y.t. Chan, P.c. ChingAbstract:A model for an adaptive time-delay estimator is proposed to improve its performance in estimating the difference in arrival time of a bandlimited random Signal received by two spatially separated sensors in an environment where the Signal and noise power are time varying. The system comprises two adaptive units: a filter to compensate time shift between the two receiver channels and a gain control to provide Wiener filtering. Both the filter coefficients and the variable gain are adjusted simultaneously by using modifications from the stochastic mean-square-error gradient in the traditional adaptive least-mean-square time-delay estimation (LMSTDE) method. The convergence characteristics of the proposed system are analyzed in detail and compared with those obtained by the traditional technique. Theoretical results show that, unlike the LMSTDE configuration, this arrangement can decouple the adaptation of time shift from the changing Signal and/or noise power, which in turn gives rise to better convergence behavior of the delay estimate. Simulation results are included to illustrate the effectiveness of the new model and corroborate the theoretical developments.
K.c. Ho - One of the best experts on this subject based on the ideXlab platform.
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adaptive time delay estimation in Nonstationary Signal and or noise power environments
IEEE Transactions on Signal Processing, 1993Co-Authors: K.c. Ho, Y.t. Chan, P.c. ChingAbstract:A model for an adaptive time-delay estimator is proposed to improve its performance in estimating the difference in arrival time of a bandlimited random Signal received by two spatially separated sensors in an environment where the Signal and noise power are time varying. The system comprises two adaptive units: a filter to compensate time shift between the two receiver channels and a gain control to provide Wiener filtering. Both the filter coefficients and the variable gain are adjusted simultaneously by using modifications from the stochastic mean-square-error gradient in the traditional adaptive least-mean-square time-delay estimation (LMSTDE) method. The convergence characteristics of the proposed system are analyzed in detail and compared with those obtained by the traditional technique. Theoretical results show that, unlike the LMSTDE configuration, this arrangement can decouple the adaptation of time shift from the changing Signal and/or noise power, which in turn gives rise to better convergence behavior of the delay estimate. Simulation results are included to illustrate the effectiveness of the new model and corroborate the theoretical developments. >
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Adaptive time-delay estimation in Nonstationary Signal and/or noise power environments
IEEE Transactions on Signal Processing, 1993Co-Authors: K.c. Ho, Y.t. Chan, P.c. ChingAbstract:A model for an adaptive time-delay estimator is proposed to improve its performance in estimating the difference in arrival time of a bandlimited random Signal received by two spatially separated sensors in an environment where the Signal and noise power are time varying. The system comprises two adaptive units: a filter to compensate time shift between the two receiver channels and a gain control to provide Wiener filtering. Both the filter coefficients and the variable gain are adjusted simultaneously by using modifications from the stochastic mean-square-error gradient in the traditional adaptive least-mean-square time-delay estimation (LMSTDE) method. The convergence characteristics of the proposed system are analyzed in detail and compared with those obtained by the traditional technique. Theoretical results show that, unlike the LMSTDE configuration, this arrangement can decouple the adaptation of time shift from the changing Signal and/or noise power, which in turn gives rise to better convergence behavior of the delay estimate. Simulation results are included to illustrate the effectiveness of the new model and corroborate the theoretical developments.
P.j.w. Rayner - One of the best experts on this subject based on the ideXlab platform.
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Blind single channel deconvolution using Nonstationary Signal processing
IEEE Transactions on Speech and Audio Processing, 2003Co-Authors: J.r. Hopgood, P.j.w. RaynerAbstract:Blind deconvolution is fundamental in Signal processing applications and, in particular, the single channel case remains a challenging and formidable problem. This paper considers single channel blind deconvolution in the case where the degraded observed Signal may be modeled as the convolution of a Nonstationary source Signal with a stationary distortion operator. The important feature that the source is Nonstationary while the channel is stationary facilitates the unambiguous identification of either the source or channel, and deconvolution is possible, whereas if the source and channel are both stationary, identification is ambiguous. The parameters for the channel are estimated by modeling the source as a time-varyng AR process and the distortion by an all-pole filter, and using the Bayesian framework for parameter estimation. This estimate can then be used to deconvolve the observed Signal. In contrast to the classical histogram approach for estimating the channel poles, where the technique merely relies on the fact that the channel is actually stationary rather than modeling it as so, the proposed Bayesian method does take account for the channel's stationarity in the model and, consequently, is more robust. The properties of this model are investigated, and the advantage of utilizing the nonstationarity of a system rather than considering it as a curse is discussed.
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Optimized support vector machines for Nonstationary Signal classification
IEEE Signal Processing Letters, 2002Co-Authors: Manuel Davy, A. Gretton, A. Doucet, P.j.w. RaynerAbstract:This letter describes an efficient method to perform Nonstationary Signal classification. A support vector machine (SVM) algorithm is introduced and its parameters optimized in a principled way. Simulations demonstrate that our low-complexity method outperforms state-of-the-art Nonstationary Signal classification techniques.
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Nonstationary Signal classification using support vector machines
Proceedings of the 11th IEEE Signal Processing Workshop on Statistical Signal Processing (Cat. No.01TH8563), 2001Co-Authors: A. Gretton, Manuel Davy, A. Doucet, P.j.w. RaynerAbstract:We demonstrate the use of support vector (SV) techniques for the binary classification of Nonstationary sinusoidal Signals with quadratic phase. We briefly describe the theory underpinning SV classification, and introduce Cohen's group time-frequency representation, which is used to process the Nonstationary Signals so as to define the classifier input space. We show that the SV classifier outperforms alternative classification methods on this processed data.
Y.t. Chan - One of the best experts on this subject based on the ideXlab platform.
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adaptive time delay estimation in Nonstationary Signal and or noise power environments
IEEE Transactions on Signal Processing, 1993Co-Authors: K.c. Ho, Y.t. Chan, P.c. ChingAbstract:A model for an adaptive time-delay estimator is proposed to improve its performance in estimating the difference in arrival time of a bandlimited random Signal received by two spatially separated sensors in an environment where the Signal and noise power are time varying. The system comprises two adaptive units: a filter to compensate time shift between the two receiver channels and a gain control to provide Wiener filtering. Both the filter coefficients and the variable gain are adjusted simultaneously by using modifications from the stochastic mean-square-error gradient in the traditional adaptive least-mean-square time-delay estimation (LMSTDE) method. The convergence characteristics of the proposed system are analyzed in detail and compared with those obtained by the traditional technique. Theoretical results show that, unlike the LMSTDE configuration, this arrangement can decouple the adaptation of time shift from the changing Signal and/or noise power, which in turn gives rise to better convergence behavior of the delay estimate. Simulation results are included to illustrate the effectiveness of the new model and corroborate the theoretical developments. >
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Adaptive time-delay estimation in Nonstationary Signal and/or noise power environments
IEEE Transactions on Signal Processing, 1993Co-Authors: K.c. Ho, Y.t. Chan, P.c. ChingAbstract:A model for an adaptive time-delay estimator is proposed to improve its performance in estimating the difference in arrival time of a bandlimited random Signal received by two spatially separated sensors in an environment where the Signal and noise power are time varying. The system comprises two adaptive units: a filter to compensate time shift between the two receiver channels and a gain control to provide Wiener filtering. Both the filter coefficients and the variable gain are adjusted simultaneously by using modifications from the stochastic mean-square-error gradient in the traditional adaptive least-mean-square time-delay estimation (LMSTDE) method. The convergence characteristics of the proposed system are analyzed in detail and compared with those obtained by the traditional technique. Theoretical results show that, unlike the LMSTDE configuration, this arrangement can decouple the adaptation of time shift from the changing Signal and/or noise power, which in turn gives rise to better convergence behavior of the delay estimate. Simulation results are included to illustrate the effectiveness of the new model and corroborate the theoretical developments.
Patrick Flandrin - One of the best experts on this subject based on the ideXlab platform.
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Nonstationary Signal Analysis with Kernel Machines
Handbook of Research on Machine Learning Applications and Trends, 2020Co-Authors: Paul Honeine, Cédric Richard, Patrick FlandrinAbstract:This chapter introduces machine learning for Nonstationary Signal analysis and classification. It argues that machine learning based on the theory of reproducing kernels can be extended to Nonstationary Signal analysis and classification. The authors show that some specific reproducing kernels allow pattern recognition algorithm to operate in the time-frequency domain. Furthermore, the authors study the selection of the reproducing kernel for a Nonstationary Signal classification problem. For this purpose, the kernel-target alignment as a selection criterion is investigated, yielding the optimal time-frequency representation for a given classification problem. These links offer new perspectives in the field of Nonstationary Signal analysis, which can benefit from recent developments of statistical learning theory and pattern recognition.
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The Sliding Singular Spectrum Analysis: A Data-Driven Nonstationary Signal Decomposition Tool
IEEE Transactions on Signal Processing, 2018Co-Authors: Jinane Harmouche, Pierre Borgnat, Dominique Fourer, François Auger, Patrick FlandrinAbstract:Singular spectrum analysis (SSA) is a Signal decomposition technique that aims at expanding Signals into interpretable and physically meaningful components (e.g., sinusoids, noise, etc.). This paper presents new theoretical and practical results about the separability of the SSA and introduces a new method called sliding SSA. First, the SSA is combined with an unsupervised classification algorithm to provide a fully automatic data-driven component extraction method for which we investigate the limitations for components separation in a theoretical study. Second, the detailed automatic SSA method is used to design an approach based on a sliding analysis window, which provides better results than the classical SSA method when analyzing Nonstationary Signals with a time-varying number of components. Finally, the proposed sliding SSA method is compared to the empirical mode decomposition and to the synchrosqueezed short-time Fourier transform, applied on both synthetic and real-world Signals.
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time frequency surrogates for Nonstationary Signal analysis
8th IMA International Conference on Mathematics in Signal Processing, 2008Co-Authors: Pierre Borgnat, Patrick FlandrinAbstract:The purpose of the present communication is, after a brief outline of the use of simple surrogates as introduced so far for stationarity tests, to deal with constructions of surrogates in ''time-frequency domains''. Indeed, for transient detection or cross-correlations analysis, one need to construct directly ''surrogate time-frequency distributions'', as opposed to distributions of surrogate time series, and keeping the `geometrical' structure in the plane of the quadratic distribution.
