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Smooth Time-Frequency Estimation using Covariance Fitting

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Author

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.

Department/s

Publishing year

2014

Language

English

Pages

779-783

Publication/Series

Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on

Document type

Conference paper

Publisher

IEEE - Institute of Electrical and Electronics Engineers Inc.

Topic

  • Probability Theory and Statistics

Keywords

  • 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

Research group

  • Statistical Signal Processing Group
  • Biomedical Modelling and Computation

ISBN/ISSN/Other

  • ISSN: 1520-6149