An adaptive optimal-kernel time-frequency representation

  • Authors:

  • D. L. Jones;R. G. Bariniuk

  • Affiliations:

  • Coordinated Sci. Lab., Illinois Univ., Urbana, IL, USA;Signal Process. Res. Centre, Queensland Univ. of Technol., Brisbane, Qld., Australia

  • Venue:
  • ICASSP '93 Proceedings of the Acoustics, Speech, and Signal Processing, 1993. ICASSP-93 Vol 4., 1993 IEEE International Conference on - Volume 04
  • Year:
  • 1993

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Abstract

Signal-dependent time-frequency representations perform well for a much wider range of signals than any fixed-kernel distribution. The time-frequency representation presented here, based on a signal-dependent radially Gaussian kernel that adapts over time, tracks signal component variations over time and supports online implementation for signals of arbitrary length. The method uses a short-time ambiguity function for kernel optimization and as an intermediate step in computing constant-time slices of the time-frequency representation. While somewhat more expensive than fixed-kernel representation, this technique often provides much better performance.