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Relevant bibliographies by topics / Time–frequency reassignment

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Academic literature on the topic 'Time–frequency reassignment'

Author: Grafiati

Published: 22 February 2023

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Journal articles on the topic "Time–frequency reassignment"

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Nilsen, G. K. "Recursive Time-Frequency Reassignment." IEEE Transactions on Signal Processing 57, no. 8 (August 2009): 3283–87. http://dx.doi.org/10.1109/tsp.2009.2020355.

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Ahrabian, Alireza, and Danilo P. Mandic. "Selective Time-Frequency Reassignment Based on Synchrosqueezing." IEEE Signal Processing Letters 22, no. 11 (November 2015): 2039–43. http://dx.doi.org/10.1109/lsp.2015.2456097.

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Auger, Francois, Patrick Flandrin, Yu-Ting Lin, Stephen McLaughlin, Sylvain Meignen, Thomas Oberlin, and Hau-Tieng Wu. "Time-Frequency Reassignment and Synchrosqueezing: An Overview." IEEE Signal Processing Magazine 30, no. 6 (November 2013): 32–41. http://dx.doi.org/10.1109/msp.2013.2265316.

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Hao, Zhi Hua, Zhuang Ma, and Hao Miao Zhou. "Research on Fault Diagnosis Method Based on Empirical Mode Decomposition & Time-Frequency Reassignment." Advanced Materials Research 433-440 (January 2012): 6256–61. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.6256.

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The reassignment method is a technique for sharpening a time-frequency representation by mapping the data to time-frequency coordinates that are nearer to the true region of support of the analyzed signal. The reassignment method has been proved to produce a better localization of the signal components and improve the readability of the time-frequency representation by concentrating its energy at a center of gravity. But there are still few cross-terms. Then, the empirical mode decomposition is introduced to the reassignment method to suppress the interference of the cross-term encountered in processing the multi-component signals. The multi-component signal can be decomposed into a finite number intrinsic mode function by using EMD. Then, the reassignment method can be calculated for each of the intrinsic mode function. Simulation analysis is presented to show that this method can improve the localization of time-frequency representation and reduce the cross terms. The vibration signals measured from diesel engine in the stage of deflagrate were analyzed with the reassignment method. Experimental results indicate that this method has good potential in mechanical fault feature extraction.

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Wei, Dahuan, Zhenfeng Huang, Hanling Mao, Xinxin Li, Huade Huang, Bang Wang, and Xiaoxu Yi. "Iterative reassignment: An energy-concentrated time-frequency analysis method." Mechanical Systems and Signal Processing 182 (January 2023): 109579. http://dx.doi.org/10.1016/j.ymssp.2022.109579.

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Bruni, Vittoria, Michela Tartaglione, and Domenico Vitulano. "A Fast and Robust Spectrogram Reassignment Method." Mathematics 7, no. 4 (April 19, 2019): 358. http://dx.doi.org/10.3390/math7040358.

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The improvement of the readability of time-frequency transforms is an important topic in the field of fast-oscillating signal processing. The reassignment method is often used due to its adaptivity to different transforms and nice formal properties. However, it strongly depends on the selection of the analysis window and it requires the computation of the same transform using three different but well-defined windows. The aim of this work is to provide a simple method for spectrogram reassignment, named FIRST (Fast Iterative and Robust Reassignment Thinning), with comparable or better precision than classical reassignment method, a reduced computational effort, and a near independence of the adopted analysis window. To this aim, the time-frequency evolution of a multicomponent signal is formally provided and, based on this law, only a subset of time-frequency points is used to improve spectrogram readability. Those points are the ones less influenced by interfering components. Preliminary results show that the proposed method can efficiently reassign spectrograms more accurately than the classical method in the case of interfering signal components, with a significant gain in terms of required computational effort.

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Samad, Salina Abdul, and Aqilah Baseri Huddin. "Improving spectrogram correlation filters with time-frequency reassignment for bio-acoustic signal classification." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 1 (April 1, 2019): 59. http://dx.doi.org/10.11591/ijeecs.v14.i1.pp59-64.

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<p>Spectrogram features have been used to automatically classify animals based on their vocalization. Usually, features are extracted and used as inputs to classifiers to distinguish between species. In this paper, a classifier based on Correlation Filters (CFs) is employed where the input features are the spectrogram image themselves. Spectrogram parameters are carefully selected based on the target dataset in order to obtain clear distinguishing images termed as call-prints. An even better representation of the call-prints is obtained using spectrogram Time-Frequency (TF) reassignment. To demonstrate the application of the proposed technique, two species of frogs are classified based on their vocalization spectrograms where for each species a correlation filter template is constructed from multiple call-prints using the Maximum Margin Correlation Filter (MMCF). The improved accuracy rate obtained with TF reassignment demonstrates that this is a viable method for bio-acoustic signal classification.</p>

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Wang, Hui, Xiu Wei Li, Yu Xin Yun, and Hai Yan Yuan. "Reassigned Time Frequency Analysis for PD Signals in GIS." Applied Mechanics and Materials 448-453 (October 2013): 1959–62. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.1959.

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Partial discharge signal in GIS is a kind of typical non-stationary signal, using the time or frequency domain simply is not enough to describe the time-varying information of PD. Based on the reason above, this paper introduces a joint time-frequency analysis method according to the reassignment theory for analyzing the PD of GIS. After the processing of the PD signals simulated and on field, we conclude that this method provides a higher concentration in the time-frequency plane and reduces the most influence of the cross-interference terms.

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Lin, Hongbo, Yue Li, Baojun Yang, and Haitao Ma. "Random denoising and signal nonlinearity approach by time-frequency peak filtering using weighted frequency reassignment." GEOPHYSICS 78, no. 6 (November 1, 2013): V229—V237. http://dx.doi.org/10.1190/geo2012-0432.1.

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Time-frequency peak filtering (TFPF) may efficiently suppress random noise and hence improve the signal-to-noise ratio. However, the errors are not always satisfactory when applying the TFPF to fast-varying seismic signals. We begin with an error analysis for the TFPF by using the spread factor of the phase and cumulants of noise. This analysis shows that the nonlinear signal component and non-Gaussian random noise lead to the deviation of the pseudo-Wigner-Ville distribution (PWVD) peaks from the instantaneous frequency. The deviation introduces the signal distortion and random oscillations in the result of the TFPF. We propose a weighted reassigned smoothed PWVD with less deviation than PWVD. The proposed method adopts a frequency window to smooth away the residual oscillations in the PWVD, and incorporates a weight function in the reassignment which sharpens the time-frequency distribution for reducing the deviation. Because the weight function is determined by the lateral coherence of seismic data, the smoothed PWVD is assigned to the accurate instantaneous frequency for desired signal components by weighted frequency reassignment. As a result, the TFPF based on the weighted reassigned PWVD (TFPF_WR) can be more effective in suppressing random noise and preserving signal as compared with the TFPF using the PWVD. We test the proposed method on synthetic and field seismic data, and compare it with a wavelet-transform method and [Formula: see text] prediction filter. The results show that the proposed method provides better performance over the other methods in signal preserving under low signal-to-noise ratio.

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Xiao, Jun, and Patrick Flandrin. "Multitaper Time-Frequency Reassignment for Nonstationary Spectrum Estimation and Chirp Enhancement." IEEE Transactions on Signal Processing 55, no. 6 (June 2007): 2851–60. http://dx.doi.org/10.1109/tsp.2007.893961.

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Dissertations / Theses on the topic "Time–frequency reassignment"

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Xiao, Jun. "Contributions to nonstationary spectrum estimation and stationary tests in the time-frequency plane." Lyon, École normale supérieure (sciences), 2008. http://www.theses.fr/2008ENSL0460.

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Pham, Duong Hung. "Contributions to the analysis of multicomponent signals : synchrosqueezing and associated methods." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAM044/document.

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De nombreux signaux physiques incluant des signaux audio (musique, parole), médicaux (ECG, PCG), de mammifères marins ou d'ondes gravitationnelles peuvent être modélisés comme une superposition d'ondes modulées en amplitude et en fréquence (modes AM-FM), appelés signaux multicomposantes (SMCs). L'analyse temps-fréquence (TF) joue un rôle central pour la caractérisation de tels signaux et, dans ce cadre, diverses méthodes ont été développées au cours de la dernière décennie. Néanmoins, ces méthodes souffrent d'une limitation intrinsèque appelée le principe d'incertitude. Dans ce contexte, la méthode de réallocation (MR) a été développée visant à améliorer les représentations TF (RTFs) données respectivement par la transformée de Fourier à court terme (TFCT) et la transformée en ondelette continue (TOC), en les concentrant autour des lignes de crête correspondant aux fréquences instantanées. Malheureusement, elle ne permet pas de reconstruction des modes, contrairement à sa variante récente connue sous le nom de transformée synchrosqueezée (TSS). Toutefois, de nombreux problèmes associés à cette dernière restent encore à traiter tels que le traitement des fortes modulations en fréquence, la reconstruction des modes d'un SMC à partir de sa TFCT sous-échantillonnée or l'estimation des signatures TF de modes irréguliers et discontinus. Cette thèse traite principalement de tels problèmes afin de construire des nouvelles méthodes TF inversibles plus puissantes et précises pour l'analyse des SMCs.Cette thèse offre six nouvelles contributions précieuses. La première contribution introduit une extension de TSS d'ordre deux appliqué à la TOC ainsi qu'une discussion sur son analyse théorique et sa mise en œuvre pratique. La seconde contribution propose une généralisation des techniques de synchrosqueezing construites sur la TFCT, connue sous le nom de transformée synchrosqueezée d'ordre supérieur (FTSSn), qui permet de mieux traiter une large gamme de SMCs. La troisième contribution propose une nouvelle technique utilisant sur la transformée synchrosqueezée appliquée à la TFCT de second ordre (FTSS2) et une procédure de démodulation, appelée DTSS2, conduisant à une meilleure performance de la reconstruction des modes. La quatrième contribution est celle d'une nouvelle approche permettant la récupération des modes d'un SMC à partir de sa TFCT sous-échantillonnée. La cinquième contribution présente une technique améliorée, appelée calcul de représentation des contours adaptatifs (CRCA), utilisée pour une estimation efficace des signatures TF d'une plus grande classe de SMCs. La dernière contribution est celle d'une analyse conjointe entre l'CRCA et la factorisation matricielle non-négative (FMN) pour un débruitage performant des signaux phonocardiogrammes (PCG)
Many physical signals including audio (music, speech), medical data (ECG, PCG), marine mammals or gravitational-waves can be accurately modeled as a superposition of amplitude and frequency-modulated waves (AM-FM modes), called multicomponent signals (MCSs). Time-frequency (TF) analysis plays a central role in characterizing such signals and in that framework, numerous methods have been proposed over the last decade. However, these methods suffer from an intrinsic limitation known as the uncertainty principle. In this regard, reassignment method (RM) was developed with the purpose of sharpening TF representations (TFRs) given respectively by the short-time Fourier transform (STFT) or the continuous wavelet transform (CWT). Unfortunately, it did not allow for mode reconstruction, in opposition to its recent variant known as synchrosqueezing transforms (SST). Nevertheless, many critical problems associated with the latter still remain to be addressed such as the weak frequency modulation condition, the mode retrieval of an MCS from its downsampled STFT or the TF signature estimation of irregular and discontinuous signals. This dissertation mainly deals with such problems in order to provide more powerful and accurate invertible TF methods for analyzing MCSs.This dissertation gives six valuable contributions. The first one introduces a second-order extension of wavelet-based SST along with a discussion on its theoretical analysis and practical implementation. The second one puts forward a generalization of existing STFT-based synchrosqueezing techniques known as the high-order STFT-based SST (FSSTn) that enables to better handle a wide range of MCSs. The third one proposes a new technique established on the second-order STFT-based SST (FSST2) and demodulation procedure, called demodulation-FSST2-based technique (DSST2), enabling a better performance of mode reconstruction. The fourth contribution is that of a novel approach allowing for the retrieval of modes of an MCS from its downsampled STFT. The fifth one presents an improved method developed in the reassignment framework, called adaptive contour representation computation (ACRC), for an efficient estimation of TF signatures of a larger class of MCSs. The last contribution is that of a joint analysis of ACRC with non-negative matrix factorization (NMF) to enable an effective denoising of phonocardiogram (PCG) signals

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Muševič, Sašo. "Non-stationary sinusoidal analysis." Doctoral thesis, Universitat Pompeu Fabra, 2013. http://hdl.handle.net/10803/123809.

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Muchos tipos de señales que encontramos a diario pertenecen a la categoría de sinusoides no estacionarias. Una gran parte de esas señales son sonidos que presentan una gran variedad de características: acústicos/electrónicos, sonidos instrumentales harmónicos/impulsivos, habla/canto, y la mezcla de todos ellos que podemos encontrar en la música. Durante décadas la comunidad científica ha estudiado y analizado ese tipo de señales. El motivo principal es la gran utilidad de los avances científicos en una gran variedad de áreas, desde aplicaciones médicas, financiera y ópticas, a procesado de radares o sonar, y también a análisis de sistemas. La estimación precisa de los parámetros de sinusoides no estacionarias es una de las tareas más comunes en procesado digital de señales, y por lo tanto un elemento fundamental e indispensable para una gran variedad de aplicaciones. Las transformaciones de tiempo y frecuencia clásicas son solamente apropiadas para señales con variación lenta de amplitud y frecuencia. Esta suposición no suele cumplirse en la práctica, lo que conlleva una degradación de calidad y la aparición de artefactos. Además, la resolución temporal y frecuencial no se puede incrementar arbitrariamente debido al conocido principio de incertidumbre de Heisenberg. \\ El principal objetivo de esta tesis es revisar y mejorar los métodos existentes para el análisis de sinusoides no estacionarias, y también proponer nuevas estrategias y aproximaciones. Esta disertación contribuye sustancialmente a los análisis sinusoidales existentes: a) realiza una evaluación crítica del estado del arte y describe con gran detalle los métodos de análisis existentes, b) aporta mejoras sustanciales a algunos de los métodos existentes más prometedores, c) propone varias aproximaciones nuevas para el análisis de los modelos sinusoidales existentes i d) propone un modelo sinusoidal muy general y flexible con un algoritmo de análisis directo y rápido.
Many types of everyday signals fall into the non-stationary sinusoids category. A large family of such signals represent audio, including acoustic/electronic, pitched/transient instrument sounds, human speech/singing voice, and a mixture of all: music. Analysis of such signals has been in the focus of the research community for decades. The main reason for such intense focus is the wide applicability of the research achievements to medical, financial and optical applications, as well as radar/sonar signal processing and system analysis. Accurate estimation of sinusoidal parameters is one of the most common digital signal processing tasks and thus represents an indispensable building block of a wide variety of applications. Classic time-frequency transformations are appropriate only for signals with slowly varying amplitude and frequency content - an assumption often violated in practice. In such cases, reduced readability and the presence of artefacts represent a significant problem. Time and frequency resolu

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顏旭志. "An Improved Algorithm of Reassignment in Time-frequency Analysis." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/91350579256361190642.

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碩士
國立海洋大學
電機工程學系
88
There are many time-frequency distribution (TFD) methods to analyze nonstationary signal, for example, short-time Fourier transform, Wigner-Ville distribution (WVD), polynomial WVD (PWVD), and high-order L-Wigner distribution (HOLWD). Unfortunately, these methods can''t have good performancein many situations. The development of modified and adaptive PWVD and LWD can overcome some problems, but there are still weak in some specific signals.The shortcomings make modified and adaptive methods lose their universality. In order to increase their versatilities, we present the reassigned TFD method.When the choiced TFD produces less accurate time-frequency localization in full or partial signal, we assign TFD window center into signal''s gravity of energy and analyze again.The TFD values can converge toward the exact instantaneous frequency (IF) of signal, and the resolution is improved. Taking advantages of the revised method, we can get more concentrated TFD results. In addition, after TFD analyzing, we can utilize the IF estimated values to reconstruct original signal.

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TARTAGLIONE, Michela. "Analysis and decomposition of frequency modulated multicomponent signals." Doctoral thesis, 2021. http://hdl.handle.net/11573/1516269.

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Frequency modulated (FM) signals are studied in many research fields, including seismology, astrophysics, biology, acoustics, animal echolocation, radar and sonar. They are referred as multicomponent signals (MCS), as they are generally composed of multiple waveforms, with specific time-dependent frequencies, known as instantaneous frequencies (IFs). Many applications require the extraction of signal characteristics (i.e. amplitudes and IFs). that is why MCS decomposition is an important topic in signal processing. It consists of the recovery of each individual mode and it is often performed by IFs separation. The task becomes very challenging if the signal modes overlap in the TF domain, i.e. they interfere with each other, at the so-called non-separability region. For this reason, a general solution to MCS decomposition is not available yet. As a matter of fact, the existing methods addressing overlapping modes share the same limitations: they are parametric, therefore they adapt only to the assumed signal class, or they rely on signal-dependent and parametric TF representations; otherwise, they are interpolation techniques, i.e. they almost ignore the information corrupted by interference and they recover IF curve by some fitting procedures, resulting in high computational cost and bad performances against noise. This thesis aims at overcoming these drawbacks, providing efficient tools for dealing with MCS with interfering modes. An extended state-of-the-art revision is provided, as well as the mathematical tools and the main definitions needed to introduce the topic. Then, the problem is addressed following two main strategies: the former is an iterative approach that aims at enhancing MCS' resolution in the TF domain; the latter is a transform-based approach, that combines TF analysis and Radon Transform for separating individual modes. As main advantage, the methods derived from both the iterative and the transform-based approaches are non-parametric, as they do not require specific assumptions on the signal class. As confirmed by the experimental results and the comparative studies, the proposed approach contributes to the current state of the-art improvement.

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Book chapters on the topic "Time–frequency reassignment"

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Chassande-Mottin, Eric, Francois Auger, and Patrick Flandrin. "Time-Frequency/Time-Scale Reassignment." In Wavelets and Signal Processing, 233–67. Boston, MA: Birkhäuser Boston, 2003. http://dx.doi.org/10.1007/978-1-4612-0025-3_8.

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Chassande-Mottin, Eric, Patrick Flandrin, and François Auger. "On the Statistics of Spectrogram Reassignment Vectors." In Recent Developments in Time-Frequency Analysis, 23–30. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2838-5_3.

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Shan, Pei-Wei, and Ming Li. "A Study of Nonlinear Time–Varying Spectral Analysis Based on HHT, MODWPT and Multitaper Time–Frequency Reassignment." In Computational Science and Its Applications – ICCSA 2010, 191–205. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12165-4_16.

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Flandrin, P., F. Auger, and E. Chassande-Mottin. "Time‚ÄìFrequency Reassignment." In Applications in Time-Frequency Signal Processing, 179–203. CRC Press, 2002. http://dx.doi.org/10.1201/9781420042467.ch5.

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Flandrin, P., F. Auger, and E. Chassande-Mottin. "Time-Frequency Reassignment: From Principles to Algorithms." In Applications in Time-Frequency Signal Processing, 179–204. CRC Press, 2018. http://dx.doi.org/10.1201/9781315220017-5.

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Conference papers on the topic "Time–frequency reassignment"

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Li, Xiumei, and Guoan Bi. "Reassignment methods for robust time-frequency representations." In Signal Processing (ICICS). IEEE, 2009. http://dx.doi.org/10.1109/icics.2009.5397517.

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Meignen, S., T. Gardner, and T. Oberlin. "Time-frequency ridge analysis based on the reassignment vector." In 2015 23rd European Signal Processing Conference (EUSIPCO). IEEE, 2015. http://dx.doi.org/10.1109/eusipco.2015.7362631.

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Pei-Wei Shan and Ming Li. "Time-frequency analysis system based on reassignment with multitapering." In 2009 Fifth International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control (ICSCCW). IEEE, 2009. http://dx.doi.org/10.1109/icsccw.2009.5379456.

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Sejdic, Ervin, Umut Ozertem, Igor Djurovic, and Deniz Erdogmus. "A new approach for the reassignment of time-frequency representations." In ICASSP 2009 - 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2009. http://dx.doi.org/10.1109/icassp.2009.4960254.

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Bruni, Vittoria, Michela Tartaglione, and Domenico Vitulano. "On the time-frequency reassignment of interfering modes in multicomponent FM signals." In 2018 26th European Signal Processing Conference (EUSIPCO). IEEE, 2018. http://dx.doi.org/10.23919/eusipco.2018.8553498.

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Wang Danzhi, Li Shujian, and Shao Dingrong. "The analysis of frequency-hopping signal acquisition based on Cohen-reassignment joint time-frequency distribution." In Proceedings. Asia-Pacific Conference on Environmental Electromagnetics. IEEE, 2003. http://dx.doi.org/10.1109/ceem.2003.238474.

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Lin, Yu-Ting, Huey-Wen Yien, Shu-Shya Hseu, and Jenho Tsao. "Analyzing autonomic activity in electrocardiography about general anesthesia by spectrogram with multitaper time-frequency reassignment." In 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2011. http://dx.doi.org/10.1109/bmei.2011.6098432.

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Anvari, Rasoul, Adil Hussein Mohammed, and Shima Rashidi. "Seismic low-frequency shadow detection based on the Levenberg-Marquardt reassignment operators using S-transforms." In 4th International Conference on Communication Engineering and Computer Science (CIC-COCOS’2022). Cihan University, 2022. http://dx.doi.org/10.24086/cocos2022/paper.632.

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Based on the phenomenon of low-frequency shadow beneath the oil and gas reservoirs, there is much theoretical research and practical production data. Therefore, accurate detection of the low-frequency shadow in order to predict reservoir. The high-precision detection time-frequency transform in this paper is achieved by adding Levenberg-Marquardt reassignment operators using S-transforms to adjust the window width adaptively according to the characteristics of different signal components. Finally, it optimizes the time-frequency distribution. Simulation results show that this method has a better time-frequency concentration than conventional methods. Finally, an application of this method in detecting low-frequency shadow verifies the effectiveness and feasibility, which provides a high-precision tool and means for reservoir prediction.

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Swiercz, Ewa. "Application of the reassignment of time-frequency distributions to Doppler radar tomography imaging of a rotating multi-point object." In 2016 17th International Radar Symposium (IRS). IEEE, 2016. http://dx.doi.org/10.1109/irs.2016.7497311.

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Feltane, Amal, G. F. Boudreaux Bartels, Yacine Boudria, and Walter Besio. "Analyzing the presence of chirp signals in the electroencephalogram during seizure using the reassignment time-frequency representation and the Hough transform." In 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2013. http://dx.doi.org/10.1109/ner.2013.6695903.

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