The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
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.
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“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]
Citations
Cites
Generalized Phase-Space Distribution Functions
Cohen, L. W. (1966) · Journal of Mathematical Physics · doi:10.1063/1.1931206 · Cited by 1,102
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
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Altes, R. A. (1980) · The Journal of the Acoustical Society of America · doi:10.1121/1.384165 · Cited by 264
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
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
Self-citationFlandrin, P., & Escudié, B. (1980) · Signal Processing · doi:10.1016/0165-1684(80)90001-8 · Cited by 40
Self-citation
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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 3,593
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
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
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
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
Metadata
- 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 › Computer Science › Signal Processing › 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
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