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

Everitt, R. G., Andrieu, C. and Davy, M. (2013) Online bayesian inference in some time-frequency representations of non-stationary processes. IEEE Transactions on Signal Processing, 61 (22). pp. 5755-5766. ISSN 1053-587X

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To link to this item DOI: 10.1109/TSP.2013.2280128

Abstract/Summary

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.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics > Applied Statistics
ID Code:33926
Uncontrolled Keywords:Signal processing algorithms, particle filters, spectrogram, Bayesian methods, frequency domain analysis.
Publisher:IEEE
Publisher Statement:(c) 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

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