A signal-dependent evolution kernel for cohen class time-frequency distributions

N. Sudarshan Rao, P. S. Moharir

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Cohen class time-frequency distributions serve as alternatives to the traditional spectrogram and are known for their ability to provide simultaneous resolution in time and frequency. They employ a kernel along with the signal's Wigner distribution. Kernel design has witnessed significant attention. Very recently Costa and Boudreaux-Bartels have proposed a multiform tiltable exponential distribution kernel containing six parameters. This paper presents optimization of these parameters using evolution programs.

Original languageEnglish (US)
Article numberSP980313
Pages (from-to)158-165
Number of pages8
JournalDigital Signal Processing: A Review Journal
Issue number3
StatePublished - 1998

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Statistics, Probability and Uncertainty
  • Computational Theory and Mathematics
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Applied Mathematics


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