Online bayesian inference in some time-frequency representations of non-stationary processes

Richard Geoffrey Everitt, Christophe Andrieu, Manuel Davy

Research output: Contribution to journalArticle (Academic Journal)

5 Citations (Scopus)

Abstract

The use of Bayesian inference in the inference of time-frequency representations has, thus far, been limited to offline analysis of signals, using a smoothing spline based model of the time-frequency plane. In this paper we introduce a new framework that allows the routine use of Bayesian inference for online estimation of the time-varying spectral density of a locally stationary Gaussian process. The core of our approach is the use of a likelihood inspired by a local Whittle approximation. This choice, along with the use of a recursive algorithm for non-parametric estimation of the local spectral density, permits the use of a particle filter for estimating the time-varying spectral density online. We provide demonstrations of the algorithm through tracking chirps and the analysis of musical data.

Original languageEnglish
Article number6587847
Pages (from-to)5755-5766
Number of pages12
JournalIEEE Transactions on Signal Processing
Volume61
Issue number22
DOIs
Publication statusPublished - 2013

Keywords

  • Bayesian methods
  • DSP-TFSR
  • Frequency domain analysis EDICS Categories
  • MLR-BAYL
  • MLR-MUSI
  • Particle filters
  • Signal processing algorithms
  • Spectrogram
  • SSP-NSSP
  • SSPTRAC

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