Computationally efficient scale covariant time-frequency distributions

Authors:
Selin Aviyente
Affiliations:
Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI
Venue:
EURASIP Journal on Advances in Signal Processing
Year:
2009

Citing 3
Cited 0

Time-frequency analysis: theory and applications

Time-frequency analysis: theory and applications
Signal processing and pattern recognition in scale-content domains: theory and applications

Signal processing and pattern recognition in scale-content domains: theory and applications
A centrosymmetric kernel decomposition for time-frequency distribution computation

IEEE Transactions on Signal Processing

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Abstract

Scale is a physical attribute of a signal which occurs in many natural settings. Time-frequency distributions (TFDs) belonging to Cohen's class are invariant to time and frequency shifts, but are not necessarily covariant to the time scalings of the signal. Conditions on the time-frequency kernel for yielding a scale covariant distribution have been previously derived (Cohen, 1995). In this paper, a new class of computationally efficient scale covariant distributions is introduced. These distributions are constructed using the eigendecomposition of time-frequency kernels (Burrus et al., 1997). The performance of this new class of distributions is illustrated with examples and is compared to conventional scale covariant distributions.