Some features of time-frequency representations of multicomponent signals
Abstract
The Wigner-Ville Distribution (WVD) is now known to be a convenient tool for the time-frequency analysis of non-stationary signals, and especially monocomponent ones. However, in the case of multicomponent signals, its bilinear structure is also known to create cross-terms without any physical significance. Starting with the general formulation of time-frequency representations, which only depend on an arbitrary kernel function, we first characterize properties of such cross-terms and then propose appropriate smoothings of the WVD in order to reduce their influence. Such suitable and versatile approximations are compared on synthetic and natural signals and an extension to time-frequency filtering is proposed.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The STFT family provides cross-term free time-frequency representations.
“The STFT family provides cross-term free time-frequency representations.”
From Short-time fourier transform: two fundamental properties and an optimal implementation (2003) · cites this paper as [10]
Cites
Generalized Phase-Space Distribution Functions
Cohen, L. W. (1966) · Journal of Mathematical Physics · doi:10.1063/1.1931206 · Cited by 1,111 · see citing works
Semi-classical mechanics in phase space: A study of Wigner’s function
Berry, M. (1977) · Philosophical Transactions of the Royal Society of London Series A Mathematical and Physical Sciences · doi:10.1098/rsta.1977.0145 · Cited by 581 · see citing works
Detection, estimation, and classification with spectrograms
Altes, R. A. (1980) · The Journal of the Acoustical Society of America · doi:10.1121/1.384165 · Cited by 264 · see citing works
Time-Frequency Distributions of Loudspeakers: The Application of the Wigner Distribution
Janse, C. P., & Kaizer, A. J. (1983) · Journal of the Audio Engineering Society · Cited by 102 · see citing works
An interpretation of the Pseudo-Wigner-Ville distribution
Flandrin, P., & Escudié, B. (1984) · Signal Processing · doi:10.1016/0165-1684(84)90048-3 · Cited by 60 · see citing works
Self-citationFlandrin, P., & Escudié, B. (1980) · Signal Processing · doi:10.1016/0165-1684(80)90001-8 · Cited by 40 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Time-frequency distributions-a review
Cohen, L. W. (1989) · Proceedings of the IEEE · doi:10.1109/5.30749 · Cited by 3,580 · see citing works
Linear and quadratic time-frequency signal representations
Hlawatsch, F., & Boudreaux-Bartels, G. F. (1992) · IEEE Signal Processing Magazine · doi:10.1109/79.127284 · Cited by 1,632 · see citing works
Improved time-frequency representation of multicomponent signals using exponential kernels
Choi, H., & Williams, W. J. (1989) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/assp.1989.28057 · Cited by 1,301 · see citing works
Wigner-Ville spectral analysis of nonstationary processes
Martin, W., & Flandrin, P. (1985) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1985.1164760 · Cited by 554 · see citing works
Self-citationZhao, Y., Atlas, L., & Marks, R. J. (1990) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/29.57537 · Cited by 430 · see citing works
Kernel design for reduced interference distributions
Jeong, J., & Williams, W. J. (1992) · IEEE Transactions on Signal Processing · doi:10.1109/78.124950 · Cited by 403 · see citing works
An adaptive optimal-kernel time-frequency representation
Jones, D. L., & Baraniuk, R. G. (1995) · IEEE Transactions on Signal Processing · doi:10.1109/78.469854 · Cited by 389 · see citing works
Time-Frequency Signal Analysis and Processing
Boashash, B. (2016) · Elsevier eBooks · doi:10.1016/c2012-0-00024-5 · Cited by 376 · see citing works
Time-scale energy distributions: a general class extending wavelet transforms
Rioul, O., & Flandrin, P. (1992) · IEEE Transactions on Signal Processing · doi:10.1109/78.143446 · Cited by 362 · see citing works
Self-citationShort-time fourier transform: two fundamental properties and an optimal implementation
Durak, L., & Arikan, O. (2003) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2003.810293 · Cited by 345 · see citing works
A signal-dependent time-frequency representation: optimal kernel design
Baraniuk, R. G., & Jones, D. L. (1993) · IEEE Transactions on Signal Processing · doi:10.1109/78.212733 · Cited by 290 · see citing works
Time-Frequency Energy Distributions Meet Compressed Sensing
Flandrin, P., & Borgnat, P. (2010) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2010.2044839 · Cited by 266 · see citing works
Self-citationTime-varying filtering and signal estimation using Wigner distribution synthesis techniques
Boudreaux-Bartels, G. F., & Parks, T. W. (1986) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1986.1164833 · Cited by 260 · see citing works
Automatic LPI Radar Waveform Recognition Using CNN
Kong, S.-H., Kim, M.-J., Hoang, L., & Kim, E. (2018) · IEEE Access · doi:10.1109/access.2017.2788942 · Cited by 179 · see citing works
Signal-dependent time-frequency analysis using a radially Gaussian kernel
Baraniuk, R. G., & Jones, D. L. (1993) · Signal Processing · doi:10.1016/0165-1684(93)90001-q · Cited by 165 · see citing works
A resolution comparison of several time-frequency representations
Jones, D. L., & Parks, T. W. (1992) · IEEE Transactions on Signal Processing · doi:10.1109/78.124951 · Cited by 162 · see citing works
Alias-free generalized discrete-time time-frequency distributions
Jeong, J., & Williams, W. J. (1992) · IEEE Transactions on Signal Processing · doi:10.1109/78.165662 · Cited by 140 · see citing works
Detecting ventricular fibrillation
Afonso, V. X., & Tompkins, W. J. (1995) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/51.376752 · Cited by 133 · see citing works
Papandreou, A., Hlawatsch, F., & Boudreaux-Bartels, G. F. (1993) · IEEE Transactions on Signal Processing · doi:10.1109/78.258084 · Cited by 121 · see citing works
Peyrin, F., & Prost, R. (1986) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1986.1164880 · Cited by 104 · see citing works
Joint spatial/spatial-frequency representation
Jacobson, L. D., & Wechsler, H. (1988) · Signal Processing · doi:10.1016/0165-1684(88)90043-6 · Cited by 98 · see citing works
Kernel design for time-frequency signal analysis using the Radon transform
Ristić, B., & Boashash, B. (1993) · IEEE Transactions on Signal Processing · doi:10.1109/78.215325 · Cited by 97 · see citing works
Mainardi, L. T. (2008) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2008.0188 · Cited by 96 · see citing works
Reduced interference distributions: biological applications and interpretations
Williams, W. J. (1996) · Proceedings of the IEEE · doi:10.1109/5.535245 · Cited by 93 · see citing works
Bilinear time-frequency representations: new insights and properties
Loughlin, P. J., Pitton, J. W., & Atlas, L. (1993) · IEEE Transactions on Signal Processing · doi:10.1109/78.193215 · Cited by 92 · see citing works
Hlawatsch, F., Manickam, T., Urbanke, R. L., & Jones, W. (1995) · Signal Processing · doi:10.1016/0165-1684(94)00150-x · Cited by 90 · see citing works
Derivation of optical flow using a spatiotemporal-Frequency approach
Jacobson, L. D., & Wechsler, H. (1987) · Computer Vision Graphics and Image Processing · doi:10.1016/s0734-189x(87)80152-4 · Cited by 90 · see citing works
Explorations in Time-Frequency Analysis
Flandrin, P. (2018) · Cambridge University Press eBooks · doi:10.1017/9781108363181 · Cited by 87 · see citing works
Self-citationAuto-term representation by the reduced interference distributions: a procedure for kernel design
Stanković, L. (1996) · IEEE Transactions on Signal Processing · doi:10.1109/78.506622 · Cited by 86 · see citing works
Geometry of Affine Time–Frequency Distributions
Flandrin, P., & Gonçalvès, P. (1996) · Applied and Computational Harmonic Analysis · doi:10.1006/acha.1996.0002 · Cited by 79 · see citing works
Self-citationAn analysis of some time-frequency and time-scale distributions
Stanković, L. (1994) · Annals of Telecommunications · doi:10.1007/bf02999442 · Cited by 70 · see citing works
Lopac, N., Hržić, F., Vuksanović, I. P., & Lerga, J. (2021) · IEEE Access · doi:10.1109/access.2021.3139850 · Cited by 70 · see citing works
New time-frequency distributions: theory and applications
Williams, W. J., & Jeong, J. (2003) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.1989.100580 · Cited by 69 · see citing works
Time-frequency analysis of electrophysiology signals in epilepsy
Williams, W. J., Zaveri, H. P., & Sackellares, J. C. (1995) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/51.376750 · Cited by 65 · see citing works
A radially-Gaussian, signal-dependent time-frequency representation
Baraniuk, R. G., & Jones, D. L. (1991) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1991.150131 · Cited by 65 · see citing works
Affine smoothing of the Wigner-Ville distribution
Flandrin, P., & Rioul, O. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1990.116088 · Cited by 60 · see citing works
Self-citationSong, K. Y., Petrou, M., & Kittler, J. (1995) · Machine Vision and Applications · doi:10.1007/bf01213639 · Cited by 59 · see citing works
Human Activity Recognition From FMCW Radar Signals Utilizing Cross-Terms Free WVD
Faisal, K. N., Mir, H., & Sharma, R. R. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3344100 · Cited by 56 · see citing works
DEVELOPMENT OF ENHANCED WIGNER–VILLE DISTRIBUTION FUNCTION
Lee, J.-H., Kim, J., & Kim, H. (2001) · Mechanical Systems and Signal Processing · doi:10.1006/mssp.2000.1365 · Cited by 55 · see citing works
Design of time-frequency representations using a multiform, tiltable exponential kernel
Costa, A. H., & Boudreaux-Bartels, G. F. (1995) · IEEE Transactions on Signal Processing · doi:10.1109/78.469860 · Cited by 54 · see citing works
Kernel decomposition of time-frequency distributions
Cunningham, G. S., & Williams, W. J. (1994) · IEEE Transactions on Signal Processing · doi:10.1109/78.286958 · Cited by 53 · see citing works
Singru, P. M., Krishnakumar, V., Natarajan, D., & Raizada, A. (2018) · Journal of Vibroengineering · doi:10.21595/jve.2017.17768 · Cited by 51 · see citing works
Williams, W. J., Zaveri, H. P., & Sackellares, J. C. (1995) · Cited by 50 · see citing works
Regularity and unitarity of bilinear time-frequency signal representations
Hlawatsch, F. (1992) · IEEE Transactions on Information Theory · doi:10.1109/18.108252 · Cited by 50 · see citing works
Duality and classification of bilinear time-frequency signal representations
Hlawatsch, F. (1991) · IEEE Transactions on Signal Processing · doi:10.1109/78.134395 · Cited by 49 · see citing works
Fast computation of the ambiguity function and the Wigner distribution on arbitrary line segments
Özdemir, A. K., & Arıkan, O. (2001) · IEEE Transactions on Signal Processing · doi:10.1109/78.902121 · Cited by 44 · see citing works
L-class of time-frequency distributions
Stanković, L. (1996) · IEEE Signal Processing Letters · doi:10.1109/97.475827 · Cited by 42 · see citing works
Time-Frequency Design and Processing of Signals Via Smoothed Wigner Distributions
Krattenthaler, W., & Hlawatsch, F. (1993) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.1993.193145 · Cited by 42 · see citing works
Influence of the resonant frequency on the control of knock in diesel engines
Ren, Y., Randall, R. B., & Milton, B. E. (1999) · Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering · doi:10.1243/0954407991526748 · Cited by 41 · see citing works
A Tutorial on Non-Parametric Bilinear Time-Frequency Signal Representations
Mecklenbräuker, W. (1989) · Springer eBooks · doi:10.1007/978-3-7091-2620-2_2 · Cited by 41 · see citing works
Joint signal parameter estimation of frequency-hopping communications
Chen, T.-C. (2012) · IET Communications · doi:10.1049/iet-com.2010.0318 · Cited by 40 · see citing works
Flandrin, P. (1990) · Inverse problems and theoretical imaging · doi:10.1007/978-3-642-75988-8_4 · Cited by 40 · see citing works
Self-citationTime-Frequency Localization from Sparsity Constraints
Borgnat, P., & Flandrin, P. (2008) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2008.4518477 · Cited by 39 · see citing works
Self-citationInterpolated smoothed pseudo Wigner-Ville distribution for accurate spectrum analysis
Andria, G., & Savino, M. (1996) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/19.517002 · Cited by 39 · see citing works
A Deep Neural Network Approach for Classification of GNSS Interference and Jamming
Mehr, I. E., & Dovis, F. (2024) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2024.3462662 · Cited by 39 · see citing works
Adaptive cone-kernel time-frequency analysis
Czerwinski, R. N., & Jones, D. L. (1995) · IEEE Transactions on Signal Processing · doi:10.1109/78.398735 · Cited by 38 · see citing works
Djurdjanović, D., Ni, J., & Lee, J. (2002) · Dynamic Systems and Control · doi:10.1115/imece2002-32032 · Cited by 38 · see citing works
Transformation de wigner-ville: description d’un nouvel outil de traitement du signal et des images
Zhu, Y., Peyrin, F., & Goutte, R. (1987) · Annals of Telecommunications · doi:10.1007/bf02994998 · Cited by 38 · see citing works
Flandrin, P. (1989) · Inverse problems and theoretical imaging · doi:10.1007/978-3-642-97177-8_4 · Cited by 37 · see citing works
Self-citationElimination of interference terms of the discrete Wigner distribution using nonlinear filtering
Arce, G. R., & Hasan, S. R.-U. (2000) · IEEE Transactions on Signal Processing · doi:10.1109/78.852013 · Cited by 36 · see citing works
An expansion of Wigner distribution and its applications
Marinovic, N., & Eichmann, G. (2005) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1985.1168130 · Cited by 36 · see citing works
Some Theoretical Concepts for Echolocation
Altes, R. A. (1988) · Springer eBooks · doi:10.1007/978-1-4684-7493-0_78 · Cited by 33 · see citing works
Cohen, L. W. (1996) · IEEE Transactions on Signal Processing · doi:10.1109/78.502322 · Cited by 33 · see citing works
Bilinear time-frequency representations of signals: the shift-scale invariant class
Hlawatsch, F., & Urbanke, R. L. (1994) · IEEE Transactions on Signal Processing · doi:10.1109/78.275608 · Cited by 33 · see citing works
On the use of two-dimensional Wigner-Ville distribution for texture segmentation
Zhu, Y., Goutte, R., & AMIEL, M. (1993) · Signal Processing · doi:10.1016/0165-1684(93)90016-4 · Cited by 31 · see citing works
The use of a two-dimensional Hilbert transform for Wigner analysis of 2-dimensional real signals
Zhu, Y., Peyrin, F., & Goutte, R. (1990) · Signal Processing · doi:10.1016/0165-1684(90)90113-d · Cited by 31 · see citing works
A virtual instrument for time-frequency analysis
Djurović, I., & Stanković, L. (1999) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/19.816118 · Cited by 29 · see citing works
On detection-estimation procedures in the time-frequency plane
Flandrin, P. (2005) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1986.1168669 · Cited by 29 · see citing works
Self-citationWigner-Ville analysis of asymptotic signals and applications
Boashash, B., & Escudié, B. (1985) · Signal Processing · doi:10.1016/0165-1684(85)90109-4 · Cited by 28 · see citing works
An adaptive optimal-kernel time-frequency representation
Jones, D. L., & Bariniuk, R. G. (1993) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1993.319606 · Cited by 28 · see citing works
Mechanism of the cross-terms in spectrograms
Jeong, J., & Williams, W. J. (1992) · IEEE Transactions on Signal Processing · doi:10.1109/78.157305 · Cited by 28 · see citing works
Quadratic Time‐Frequency Analysis I: Cohen's Class
Auger, F. A., & Chassande-Mottin, E. (2008) · doi:10.1002/9780470611203.ch5 · Cited by 28 · see citing works
A Statistical Time-Frequency Model for Non-stationary Time Series Analysis
Luo, Y., & Wang, Y. (2020) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2020.3014607 · Cited by 27 · see citing works
Transient analysis of speech signals using the Wigner time-frequency representation
Velez, E. F., & Absher, R. G. (2003) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1989.266911 · Cited by 25 · see citing works
Fonollosa, J. R., & Nikias, C. L. (1994) · Signal Processing · doi:10.1016/0165-1684(94)90030-2 · Cited by 25 · see citing works
The Wigner distribution of a linear signal space
Hlawatsch, F., & Kozek, W. (1993) · IEEE Transactions on Signal Processing · doi:10.1109/78.205727 · Cited by 25 · see citing works
Computerized classification of temporomandibular joint sounds
Djurdjanović, D., Widmalm, S., Williams, W. J., Koh, C. K. H., & Yang, K. P. (2000) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.855924 · Cited by 24 · see citing works
Signal synthesis from pseudo-Wigner distribution and applications
Yu, K., & Cheng, S. (1987) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1987.1165285 · Cited by 23 · see citing works
Hyperbolic kernel for time-frequency power spectrum
Le, K. N., Dabke, K. P., & Egan, G. K. (2003) · Optical Engineering · doi:10.1117/1.1590651 · Cited by 21 · see citing works
On computing the smoothed Wigner distribution
Garudadri, H., Beddoes, M. P., Benguerel, A.-P., & GILBERT, J. H. V. (2005) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1987.1169861 · Cited by 21 · see citing works
Performance evaluation of the B-distribution
Sucic, V., Barkat, B., & Boashash, B. (2003) · International Symposium on Signal Processing and Its Applications (ISSPA) · doi:10.1109/isspa.1999.818164 · Cited by 20 · see citing works
Evolutions of Partial and Global Resonances in Transient Electromagnetic Scattering
Lui, H.-S., & Shuley, N. V. (2008) · IEEE Antennas and Wireless Propagation Letters · doi:10.1109/lawp.2008.2000641 · Cited by 20 · see citing works
Equivalence between two-dimensional analytic and real signal Wigner distributions
Zhu, Y., Peyrin, F., & Goutte, R. (1989) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/29.35413 · Cited by 20 · see citing works
Removing interference components in time–frequency representations using morphological operators
Gómez, S., Naranjo, V., & Miralles, R. (2011) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2011.03.007 · Cited by 19 · see citing works
Comparison of WVD based time-frequency distributions
Thomas, M., Jacob, R., & Lethakumary, B. (2012) · International Conference on Power, Signals, Controls and Computation (EPSCICON) · doi:10.1109/epscicon.2012.6175242 · Cited by 19 · see citing works
A linear model for TF distribution of signals
Nelson, D. J., & Smith, D. C. (2006) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2006.879303 · Cited by 19 · see citing works
On alias-free formulations of discrete-time Cohen's class of distributions
Morris, J. M., & Wu, D. (1996) · IEEE Transactions on Signal Processing · doi:10.1109/78.506603 · Cited by 19 · see citing works
Chen, Z., Shi, Y., Wang, Y., Li, X., Yu, X., & Shi, Y. (2023) · IEEE Access · doi:10.1109/access.2023.3271720 · Cited by 18 · see citing works
An analysis of the Wigner higher order spectra of multicomponent signals
Stanković, L. (1994) · Annals of Telecommunications · doi:10.1007/bf02999475 · Cited by 18 · see citing works
Road Type Classification Using Time-Frequency Representations of Tire Sensor Signals
Dózsa, T., Jurdana, V., Šegota, S. B., Volk, J., Radó, J., Soumelidis, A., & Kovács, P. (2024) · IEEE Access · doi:10.1109/access.2024.3382931 · Cited by 17 · see citing works
Time frequency signal analysis: Past, present and future trends
Boashash, B. (1996) · Control and dynamic systems · doi:10.1016/s0090-5267(96)80049-1 · Cited by 17 · see citing works
Smoothed Wigner-Ville parametric modeling for the analysis of nonstationary signals
Velez, E. F., & Absher, R. G. (2003) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.1989.100401 · Cited by 17 · see citing works
Elimination of cross-components of the discrete pseudo Wigner distribution via image processing
Sun, M., Li, C. C., Sekhar, L. N., & Sclabassi, R. J. (2003) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1989.266908 · Cited by 16 · see citing works
Improved Wigner–Ville distribution performance by signal decomposition and modified group delay
Narasimhan, S. V., & Nayak, M. B. (2003) · Signal Processing · doi:10.1016/j.sigpro.2003.07.011 · Cited by 16 · see citing works
A high resolution time frequency representation with significantly reduced cross-terms
Özdemir, A. K., & Arıkan, O. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2000.859054 · Cited by 16 · see citing works
On the cross-terms in spectrograms
Jeong, J., & Williams, W. J. (2002) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.1990.112433 · Cited by 16 · see citing works
Advanced Time-Frequency Signal and System Analysis
Boashash, B., Touati, S., Flandrin, P., Hlawatsch, F., Tauböck, G., Oliveira, P. M., Barroso, V., Baraniuk, R., Jones, G., Matz, G., et al. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-398499-9.00004-2 · Cited by 15 · see citing works
Self-citationAstrophysical image separation by blind time–frequency source separation methods
Özgen, M. T., Kuruoğlu, E. E., & Herranz, D. (2008) · Digital Signal Processing · doi:10.1016/j.dsp.2007.12.003 · Cited by 15 · see citing works
A function of time, frequency, lag, and Doppler
O'Neill, J. C., & Williams, W. J. (1999) · IEEE Transactions on Signal Processing · doi:10.1109/78.747784 · Cited by 15 · see citing works
Spectral estimation based on the Wigner-Ville representation
Velez, E. F., & Absher, R. G. (1990) · Signal Processing · doi:10.1016/0165-1684(90)90137-n · Cited by 15 · see citing works
- Authors
- Flandrin, P.
- Corresponding author
- Flandrin, P.
- ORCID
- Flandrin, P.
- Venue
- ICASSP '84. IEEE International Conference on Acoustics, Speech, and Signal Processing
- Published
- 2005-03-24
- Volume
- 9
- Pages
- 266-269
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W1925346143
- Cited by
- 239
- References
- 6
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 5 · 2023: 6 · 2022: 5 · 2021: 3 · 2020: 2 · 2019: 3 · 2018: 3 · 2017: 1 · 2016: 2 · 2015: 4 · 2014: 3 · 2013: 2 · 2012: 2
- FWCI
- 26.53
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Applied Mathematics › Mathematical Analysis and Transform Methods
- Physical Sciences › Engineering › Computational Mechanics › Sparse and Compressive Sensing Techniques
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Keywords
- Wigner-Ville distribution, multicomponent signals, cross terms, time-frequency representation, kernel functions, time-frequency filtering, non-stationary signals
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
