Smooth Time-Frequency Estimation using Covariance Fitting
Författare
Summary, in English
In this paper, we introduce a time-frequency spectral estimator for smooth spectra, allowing for irregularly sampled measurements. A non-parametric representation of the time dependent (TD) covariance matrix is formed by assuming that the spectrum is piecewise linear. Using this representation, the time-frequency spectrum is then estimated by solving a convex covariance fitting problem, which also, as a byproduct, provides an enhanced estimation of the TD covariance matrix. Numerical examples using simulated non-stationary processes show the preferable performance of the proposed method as compared to the classical Wigner-Ville distribution and a smoothed spectrogram.
Avdelning/ar
- Matematisk statistik
- Statistical Signal Processing Group
- Biomedical Modelling and Computation
- eSSENCE: The e-Science Collaboration
Publiceringsår
2014
Språk
Engelska
Sidor
779-783
Publikation/Tidskrift/Serie
Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on
Fulltext
- Available as PDF - 729 kB
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Dokumenttyp
Konferensbidrag
Förlag
IEEE - Institute of Electrical and Electronics Engineers Inc.
Ämne
- Probability Theory and Statistics
Nyckelord
- Time-frequency analysis
- convex optimization
- smooth
Conference name
2014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP 2014)
Conference date
2014-05-04 - 2014-05-09
Conference place
Florence, Italy
Status
Published
Forskningsgrupp
- Statistical Signal Processing Group
- Biomedical Modelling and Computation
ISBN/ISSN/Övrigt
- ISSN: 1520-6149