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Article Dans Une Revue IEEE Transactions on Signal Processing Année : 2007

Joint Segmentation of Piecewise Constant Autoregressive Processes by Using a Hierarchical Model and a Bayesian Sampling Approach

Résumé

We propose a joint segmentation algorithm for piecewise constant autoregressive (AR) processes recorded by several independent sensors. The algorithm is based on a hierarchical Bayesian model. Appropriate priors allow us to introduce correlations between the change locations of the observed signals. Numerical problems inherent to Bayesian inference are solved by a Gibbs sampling strategy. The proposed joint segmentation methodology yields improved segmentation results when compared with parallel and independent individual signal segmentations. The initial algorithm is derived for piecewise constant AR processes whose orders are fixed on each segment. However, an extension to models with unknown model orders is also discussed. Theoretical results are illustrated by many simulations conducted with synthetic signals and real arc-tracking and speech signals.
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Dates et versions

hal-04456567 , version 1 (14-02-2024)

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Nicolas Dobigeon, Jean-Yves Tourneret, Manuel Davy. Joint Segmentation of Piecewise Constant Autoregressive Processes by Using a Hierarchical Model and a Bayesian Sampling Approach. IEEE Transactions on Signal Processing, 2007, 55 (4), pp.1251-1263. ⟨10.1109/TSP.2006.889090⟩. ⟨hal-04456567⟩
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