Smooth Time-Frequency Estimation using Covariance Fitting

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding

Abstract

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.

Details

Authors
Organisations
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Probability Theory and Statistics

Keywords

  • Time-frequency analysis, convex optimization, smooth
Original languageEnglish
Title of host publicationAcoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on
PublisherIEEE--Institute of Electrical and Electronics Engineers Inc.
Pages779-783
Number of pages5
Publication statusPublished - 2014
Publication categoryResearch
Peer-reviewedYes
Event2014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP 2014) - Florence, Italy
Duration: 2014 May 42014 May 9

Publication series

Name
ISSN (Print)1520-6149

Conference

Conference2014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP 2014)
CountryItaly
CityFlorence
Period2014/05/042014/05/09

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