The Experts below are selected from a list of 3279 Experts worldwide ranked by ideXlab platform
Sylvain Meignen - One of the best experts on this subject based on the ideXlab platform.
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A Novel Time-Frequency Technique for Mode Retrieval Based on Linear Chirp Approximation
IEEE Signal Processing Letters, 2020Co-Authors: Nils Laurent, Sylvain MeignenAbstract:In this paper, we introduce a novel time-frequency technique for the retrieval of the modes of Multicomponent Signals based on linear chirp approximation. The key idea to this new technique is to design the retrieval procedure by using only information extracted in the vicinity of the ridges made by the components in the time-frequency plane. Compared with state-of-the-art methods based on time-frequency representations, the proposed approach will prove to improve the reconstruction results when applied to a monocomponent Signal and to circumvent the mode-mixing issue when the modes of a Multicomponent Signal are close in the time-frequency plane.
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Demodulation Algorithm Based on Higher Order Synchrosqueezing
2019Co-Authors: Duong-hung Pham, Sylvain MeignenAbstract:This paper addresses the problem of detecting and retrieving amplitude-and frequency-modulated (AM-FM) components or modes of a Multicomponent Signal from its time-frequency representation (TFR) corresponding to its short-time Fourier transform. For that purpose, we introduce a novel technique that combines a high order synchrosqueezing transform (FSSTN) with a demodulation procedure. Numerical results on a Multicomponent Signal, both in noise-free and noisy cases, show the benefits for mode reconstruction of the proposed approach over similar techniques that do not make use of demodulation.
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Time-Frequency Filtering Based on Model Fitting in the Time-Frequency Plane
IEEE Signal Processing Letters, 2019Co-Authors: Marcelo Colominas, Sylvain Meignen, Duong-hung PhamAbstract:The modulus of time-frequency representations, like the short-time Fourier or wavelet transforms, of a Multicomponent Signal exhibit ridges from which one usually computes estimations of the instantaneous frequencies of the modes making up the Signal. But, due to the finite frequency resolution, the estimations thus obtained are piecewise constant. Our aim in this letter is to introduce a novel method for the estimation of the instantaneous frequencies of the modes based on the modeling of the modulus of the short-time Fourier transform, and then to propose a novel technique for mode retrieval. Numerical experiments carried out on both simulated and real Signals demonstrate the benefits of the proposed approach over others based on the short-time Fourier transform.
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Retrieval of the Modes of Multicomponent Signals From Downsampled Short-Time Fourier Transform
IEEE Transactions on Signal Processing, 2018Co-Authors: Sylvain Meignen, Duong-hung PhamAbstract:In this paper, we investigate the retrieval of the modes of Multicomponent Signals from their downsampled short-time Fourier transform. To this end, we first recall Signal reconstruction techniques based on shifted downsampled short-time Fourier transform, and then explain how to adapt these to the context of the retrieval of the modes of a Multicomponent Signal. We then show, on simulated and real data, that downsampling the short-time Fourier transform does not result in a significant performance loss of the mode retrieval procedures. Finally, comparisons with recent mode retrieval techniques based on synchrosqueezing transform are carried out, the focus being put on the amount of information needed to perform the recovery of the modes.
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Theoretical analysis of the second-order synchrosqueezing transform
Applied and Computational Harmonic Analysis, 2018Co-Authors: Ratikanta Behera, Sylvain Meignen, Thomas OberlinAbstract:In this paper, we present a theoretical analysis of the synchrosqueezing transform adapted to Multicomponent Signals made of strongly frequency modulated modes, which was recently proposed in the short time Fourier transform framework [13]. Before dealing in detail with the theoretical aspect, we explain throughout numerical simulations why the hypotheses made on the modes making up the Multicomponent Signal must be different when one considers wavelet or STFT based synchrosqueezing. After having recalled the main results regarding the original synchrosqueezing transform applied to Multicomponent Signals made of modes with weak frequency modulation, we prove the same kind of results for the novel synchrosqueezing transform adapted to strongly frequency modulated modes defined in [13].
Thomas Oberlin - One of the best experts on this subject based on the ideXlab platform.
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Theoretical analysis of the second-order synchrosqueezing transform
Applied and Computational Harmonic Analysis, 2018Co-Authors: Ratikanta Behera, Sylvain Meignen, Thomas OberlinAbstract:In this paper, we present a theoretical analysis of the synchrosqueezing transform adapted to Multicomponent Signals made of strongly frequency modulated modes, which was recently proposed in the short time Fourier transform framework [13]. Before dealing in detail with the theoretical aspect, we explain throughout numerical simulations why the hypotheses made on the modes making up the Multicomponent Signal must be different when one considers wavelet or STFT based synchrosqueezing. After having recalled the main results regarding the original synchrosqueezing transform applied to Multicomponent Signals made of modes with weak frequency modulation, we prove the same kind of results for the novel synchrosqueezing transform adapted to strongly frequency modulated modes defined in [13].
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Fully adaptive mode decomposition from time-frequency ridges
2017Co-Authors: Sylvain Meignen, Thomas Oberlin, Stephen Mc LaughlinAbstract:In this paper, we consider ridge detection for Multicomponent Signal analysis. We introduce a new ridge detector based on a projection of the reassignment vector in a specific direction which is related to the geometry of the spectrogram magnitude. The ridge definition we introduce enables that of the basin of attraction associated with a ridge and then mode reconstruction. Simulations show better concentration of the information on the ridges obtained by our method compared to other existing ridge detectors that also make use of the reassignment vector.
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The Fourier-based Synchrosqueezing Transform
2013Co-Authors: Thomas Oberlin, Sylvain Meignen, Valérie PerrierAbstract:The short-time Fourier transform (STFT) and continuous wavelet transform (CWT) are intensively used to analyze and process Multicomponent Signals, ie superpositions of mod- ulated waves. The synchrosqueezing is a post-processing method which circumvents the uncertainty relations, inherent to these linear transforms, by reassigning the coefficients in scale or frequency. Originally introduced in the setting of the continuous wavelet transform, it provides a sharp, con- centrated representation, while remaining invertible. This technique received a renewed interest with the recent publi- cation of an approximation result, which provides guarantees for the decomposition of a Multicomponent Signal. This paper adapts the formulation of the synchrosqueezing to the STFT, and states a similar theoretical result. The emphasis is put on the differences with the CWT-based synchrosqueezing, and all the content is illustrated through numerical experiments.
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A novel time-frequency technique for Multicomponent Signal denoising
2013Co-Authors: Thomas Oberlin, Sylvain Meignen, Steve MclaughlinAbstract:Multicomponent Signals, i.e. superpositions of modulated waves, arise in many physical or biological systems. Exploiting the particular structure of these Signals, denoising methods based on time-frequency distributions often outperform standard techniques such as those based on diagonal estimation or sparsity approaches. Recently, a simple denoising technique based on local integration in scale of the wavelet transform was proposed. In spite of its behaviour being better compared to classical techniques for medium noise levels, it does not perform so well in other cases. We propose here a method to improve denoising behaviour based on a more accurate mode reconstruction technique. The method is detailed for time-frequency representation given by short-time Fourier and continuous wavelet transforms, with the emphasis placed on their differences.
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EUSIPCO - A novel time-frequency technique for Multicomponent Signal denoising
2013Co-Authors: Thomas Oberlin, Sylvain Meignen, Stephen MclaughlinAbstract:Multicomponent Signals, i.e. superpositions of modulated waves, arise in many physical or biological systems. Exploiting the particular structure of these Signals, denoising methods based on time-frequency distributions often outperform standard techniques such as those based on diagonal estimation or sparsity approaches. Recently, a simple denoising technique based on local integration in scale of the wavelet transform was proposed. In spite of its behaviour being better compared to classical techniques for medium noise levels, it does not perform so well in other cases. We propose here a method to improve denoising behaviour based on a more accurate mode reconstruction technique. The method is detailed for time-frequency representation given by short-time Fourier and continuous wavelet transforms, with the emphasis placed on their differences.
Qingtang Jiang - One of the best experts on this subject based on the ideXlab platform.
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analysis of an adaptive short time fourier transform based Multicomponent Signal separation method derived from linear chirp local approximation
arXiv: Numerical Analysis, 2020Co-Authors: Charles K Chui, Qingtang JiangAbstract:The synchrosqueezing transform (SST) has been developed as a powerful EMD-like tool for instantaneous frequency (IF) estimation and component separation of non-stationary Multicomponent Signals. Recently, a direct method of the time-frequency approach, called Signal separation operation (SSO), was introduced to solving the problem of Multicomponent Signal separation. While both SST and SSO are mathematically rigorous on IF estimation, SSO avoids the second step of the two-step SST method in component recovery (mode retrieval). In addition, SSO is simple: the IF of a component is estimated by a time-frequency ridge of the SSO plane; and this component is recovered by simply plugging the time-frequency ridge to the SSO operation. In recent paper "Direct Signal separation via extraction of local frequencies with adaptive time-varying parameters", after showing that the SSO operation is related to the adaptive short-time Fourier transform (STFT), the authors obtained a more accurate component recovery formula derived from the linear chirp (also called linear frequency modulation Signal) approximation at any local time and they also proposed a recovery scheme to extract the Signal components one by one with the time-varying window updated for each component. However the theoretical analysis of the recovery formula derived from linear chirp local approximation has not been studied there. In this paper, we carry out such analysis and obtain error bounds for IF estimation and component recovery. These results provide a mathematical guarantee to the proposed adaptive STFT-based non-stationary Multicomponent Signal separation method.
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Analysis of adaptive synchrosqueezing transform with a time-varying parameter
Advances in Computational Mathematics, 2020Co-Authors: Qingtang JiangAbstract:The synchrosqueezing transform (SST) was developed recently to separate the components of non-stationary Multicomponent Signals. The continuous wavelet transform-based SST (WSST) reassigns the scale variable of the continuous wavelet transform of a Signal to the frequency variable and sharpens the time-frequency representation. The WSST with a time-varying parameter, called the adaptive WSST, was introduced very recently in the paper “Adaptive synchrosqueezing transform with a time-varying parameter for non-stationary Signal separation.” The well-separated conditions of non-stationary Multicomponent Signals with the adaptive WSST and a method to select the time-varying parameter were proposed in that paper. In addition, simulation experiments in that paper show that the adaptive WSST is very promising in estimating the instantaneous frequency of a Multicomponent Signal, and in accurate component recovery. However, the theoremretical analysis of the adaptive WSST has not been studied. In this paper, we carry out such analysis and obtain error bounds for component recovery with the adaptive WSST and the 2nd-order adaptive WSST. These results provide a mathematical guarantee to non-stationary Multicomponent Signal separation with the adaptive WSST.
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Analysis of Adaptive Synchrosqueezing Transform with a Time-varying Parameter
arXiv: Signal Processing, 2020Co-Authors: Qingtang JiangAbstract:The synchrosqueezing transform (SST) was developed recently to separate the components of non-stationary Multicomponent Signals. The continuous wavelet transform-based SST (WSST) reassigns the scale variable of the continuous wavelet transform of a Signal to the frequency variable and sharpens the time-frequency representation. The WSST with a time-varying parameter, called the adaptive WSST, was introduced very recently in the paper "Adaptive synchrosqueezing transform with a time-varying parameter for non-stationary Signal separation". The well-separated conditions of non-stationary Multicomponent Signals with the adaptive WSST and a method to select the time-varying parameter were proposed in that paper. In addition, simulation experiments in that paper show that the adaptive WSST is very promising in estimating the instantaneous frequency of a Multicomponent Signal, and in accurate component recovery. However the theoretical analysis of the adaptive WSST has not been studied. In this paper, we carry out such analysis and obtain error bounds for component recovery with the adaptive WSST and the 2nd-order adaptive WSST. These results provide a mathematical guarantee to non-stationary Multicomponent Signal separation with the adaptive WSST.
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Adaptive Synchrosqueezing Transform with a Time-Varying Parameter for Non-stationary Signal Separation
Applied and Computational Harmonic Analysis, 2020Co-Authors: Haiyan Cai, Qingtang JiangAbstract:Abstract The continuous wavelet transform (CWT)-based synchrosqueezing transform (SST) is a special type of the reassignment method which not only enhances the energy concentration of CWT in the time-frequency plane, but also separates the components of Multicomponent Signals. The “bump wavelet” and Morlet's wavelet are commonly used continuous wavelets for SST. There is a parameter in these wavelets which controls the widths of the time-frequency localization window. In most literature on SST, this parameter is a fixed positive constant. In this paper, we consider the CWT with a time-varying parameter (called the adaptive CWT) and the corresponding SST (called the adaptive SST). We also introduce the 2nd-order adaptive SST. We analyze the separation conditions for non-stationary Multicomponent Signals with the local approximation of linear frequency modulation mode. We derive well-separated conditions of a Multicomponent Signal based on the adaptive CWT. We propose methods to select the time-varying parameter so that the corresponding adaptive SSTs of the components of a Multicomponent Signal have sharp representations and are well-separated. We provide comparison experimental results to demonstrate the efficiency and robustness of the proposed adaptive SST in separating components of Multicomponent Signals with fast varying frequencies.
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Adaptive Synchrosqueezing Transform with a Time-Varying Parameter for Non-stationary Signal Separation
arXiv: Signal Processing, 2018Co-Authors: Haiyan Cai, Qingtang JiangAbstract:The continuous wavelet transform (CWT) is a linear time-frequency representation and a powerful tool for analyzing non-stationary Signals. The synchrosqueezing transform (SST) is a special type of the reassignment method which not only enhances the energy concentration of CWT in the time-frequency plane, but also separates the components of Multicomponent Signals. The "bump wavelet" and Morlet's wavelet are commonly used continuous wavelets for the wavelet-based SST. There is a parameter in these wavelets which controls the widths of the time-frequency localization window. In most literature on SST, this parameter is a fixed positive constant. In this paper, we consider the CWT with a time-varying parameter (called the adaptive CWT) and the corresponding SST (called the adaptive SST) for instantaneous frequency estimation and Multicomponent Signal separation. We also introduce the 2nd-order adaptive SST. We analyze the separation conditions for non-stationary Multicomponent Signals with the local approximation of linear frequency modulation mode. We derive well-separated conditions of a Multicomponent Signal based on the adaptive CWT. We propose methods to select the time-varying parameter so that the corresponding adaptive SSTs of the components of a Multicomponent Signal have sharp representations and are well-separated, and hence the components can be recovered more accurately. We provide comparison experimental results to demonstrate the efficiency and robustness of the proposed adaptive CWT and adaptive SST in separating components of Multicomponent Signals with fast varying frequencies.
Steve Mclaughlin - One of the best experts on this subject based on the ideXlab platform.
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On Demodulation, Ridge Detection and Synchrosqueezing for Multicomponent Signals
IEEE Transactions on Signal Processing, 2017Co-Authors: Sylvain Meignen, Duong-hung Pham, Steve MclaughlinAbstract:In this paper, we present a novel technique for the retrieval of the modes of a Multicomponent Signal using a time-frequency (TF) representation of the Signal. Our approach is based on a novel ridge extraction method that takes into account the fact that the TF representation is both discrete in time and frequency, followed by a demodulation procedure. Numerical results show the benefits of the proposed approach for mode reconstruction in comparison to similar techniques that do not make use of demodulation. Furthermore, numerical investigations show that the proposed approach sharpens the TF representation on which it is built.
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A novel time-frequency technique for Multicomponent Signal denoising
2013Co-Authors: Thomas Oberlin, Sylvain Meignen, Steve MclaughlinAbstract:Multicomponent Signals, i.e. superpositions of modulated waves, arise in many physical or biological systems. Exploiting the particular structure of these Signals, denoising methods based on time-frequency distributions often outperform standard techniques such as those based on diagonal estimation or sparsity approaches. Recently, a simple denoising technique based on local integration in scale of the wavelet transform was proposed. In spite of its behaviour being better compared to classical techniques for medium noise levels, it does not perform so well in other cases. We propose here a method to improve denoising behaviour based on a more accurate mode reconstruction technique. The method is detailed for time-frequency representation given by short-time Fourier and continuous wavelet transforms, with the emphasis placed on their differences.
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Analysis of Strongly Modulated Multicomponent Signals with the Short-Time Fourier Transform
2013Co-Authors: Thomas Oberlin, Sylvain Meignen, Steve MclaughlinAbstract:This paper addresses the issue of the retrieval of the components of a Multicomponent Signal from its short-time Fourier transform. It recalls two popular reconstruction methods, and extends each of them for the case of strong frequency modulation, by taking into account the second derivative of the phase. Numerical experiments illustrate the improvement and compare the methods.
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A New Algorithm fo Multicomponent Signals Analysis based on Synchrosqueezing: With an Application to Signal Sampling and Denoising
IEEE Transactions on Signal Processing, 2012Co-Authors: Sylvain Meignen, Thomas Oberlin, Steve MclaughlinAbstract:In this paper, we address the problem of the retrieval of the components from a Multicomponent Signal using ideas from the synchrosqueezing framework. The emphasis is on the wavelet choice and we propose a novel algorithm based first on the detection of components followed by their reconstruction. Simulations illustrate how the proposed procedure compares with the empirical mode decomposition and other related methods in terms of mode-mixing. We conclude the paper by studying the sensitivity of the proposed technique to sampling and an application to Signal denoising.
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Multicomponent Signal Denoising with SynchroSqueezing
2012Co-Authors: Sylvain Meignen, Thomas Oberlin, Steve MclaughlinAbstract:In this paper, we develop a new technique based on the synchrosqueezing method to denoise Multicomponent Signals. The approach proposed is based on a two step strategy: a mode detection step followed by a reconstruction one. The emphasis is put on the robustness of the detection step in a noisy context, a key issue in the implementation of the method. Numerical applications show the improvement in terms of Multicomponent Signal denoising brought about by the proposed method over the translation-invariant wavelet thresholding.
Stephen Mclaughlin - One of the best experts on this subject based on the ideXlab platform.
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EUSIPCO - A novel time-frequency technique for Multicomponent Signal denoising
2013Co-Authors: Thomas Oberlin, Sylvain Meignen, Stephen MclaughlinAbstract:Multicomponent Signals, i.e. superpositions of modulated waves, arise in many physical or biological systems. Exploiting the particular structure of these Signals, denoising methods based on time-frequency distributions often outperform standard techniques such as those based on diagonal estimation or sparsity approaches. Recently, a simple denoising technique based on local integration in scale of the wavelet transform was proposed. In spite of its behaviour being better compared to classical techniques for medium noise levels, it does not perform so well in other cases. We propose here a method to improve denoising behaviour based on a more accurate mode reconstruction technique. The method is detailed for time-frequency representation given by short-time Fourier and continuous wavelet transforms, with the emphasis placed on their differences.
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ICASSP - Analysis of strongly modulated Multicomponent Signals with the short-time Fourier transform
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Thomas Oberlin, Sylvain Meignen, Stephen MclaughlinAbstract:This paper addresses the issue of the retrieval of the components of a Multicomponent Signal from its short-time Fourier transform. It recalls two popular reconstruction methods, and extends each of them for the case of strong frequency modulation, by taking into account the second derivative of the phase. Numerical experiments illustrate the improvement and compare the methods.
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a new algorithm for Multicomponent Signals analysis based on synchrosqueezing with an application to Signal sampling and denoising
IEEE Transactions on Signal Processing, 2012Co-Authors: Sylvain Meignen, Thomas Oberlin, Stephen MclaughlinAbstract:In this paper, we address the problem of the retrieval of the components from a Multicomponent Signal using ideas from the synchrosqueezing framework. The emphasis is on the wavelet choice and we propose a novel algorithm based first on the detection of components followed by their reconstruction. Simulations illustrate how the proposed procedure compares with the empirical mode decomposition and other related methods in terms of mode-mixing. We conclude the paper by studying the sensitivity of the proposed technique to sampling and an application to Signal denoising.
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SSP - Multicomponent Signal denoising with synchrosqueezing
2012 IEEE Statistical Signal Processing Workshop (SSP), 2012Co-Authors: Sylvain Meignen, Thomas Oberlin, Stephen MclaughlinAbstract:In this paper, we develop a new technique based on the synchrosqueezing method to denoise Multicomponent Signals. The approach proposed is based on a two step strategy: a mode detection step followed by a reconstruction one. The emphasis is put on the robustness of the detection step in a noisy context, a key issue in the implementation of the method. Numerical applications show the improvement in terms of Multicomponent Signal denoising brought about by the proposed method over the translation-invariant wavelet thresholding.
