Dictionary-based decomposition of linear mixtures of Gaussian processes

Christophe Couvreur, Yoram Bresler

Research output: Contribution to journalConference articlepeer-review

Abstract

We consider the problem of detecting and classifying an unknown number of multiple simultaneous Gaussian processes with unknown variances given a finite length observation of their sum and a dictionary of candidate models for the signals. The optimal minimum description length (MDL) detector is presented. Asymptotic and quadratic approximations of the MDL criterion are derived, and regularization algorithms for their efficient implementation are described. The performance of the algorithms is illustrated by numerical simulations. Interpretations in terms of vector quantization and in model-based spectral analysis are discussed together with applications and possible extensions.

Original languageEnglish (US)
Pages (from-to)2519-2522
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume5
StatePublished - Jan 1 1996
Externally publishedYes
EventProceedings of the 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 6) - Atlanta, GA, USA
Duration: May 7 1996May 10 1996

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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