Parametric estimation of spectrum driven by an exogenous signal

Abstract : In this paper, we introduce new parametric generative driven auto-regressive (DAR) models. DAR models provide a non-linear and non-stationary spectral estimation of a signal, conditionally to another exogenous signal. We detail how inference can be done efficiently while guaranteeing model stability. We show how model comparison and hyper-parameter selection can be done using likelihood estimates. We also point out the limits of DAR models when the exogenous signal contains too high frequencies. Finally, we illustrate how DAR models can be applied on neuro-physiologic signals to characterize phase-amplitude coupling.
Type de document :
Pré-publication, Document de travail
2017
Liste complète des métadonnées


https://hal.archives-ouvertes.fr/hal-01448603
Contributeur : Tom Dupré La Tour <>
Soumis le : mercredi 1 février 2017 - 16:39:13
Dernière modification le : vendredi 24 mars 2017 - 01:08:38

Fichier

duprelatour2017icassp.pdf
Fichiers produits par l'(les) auteur(s)

Licence


Distributed under a Creative Commons Paternité 4.0 International License

Identifiants

  • HAL Id : hal-01448603, version 1

Citation

Tom Dupré La Tour, Yves Grenier, Alexandre Gramfort. Parametric estimation of spectrum driven by an exogenous signal. 2017. <hal-01448603v1>

Partager

Métriques

Consultations de
la notice

160

Téléchargements du document

73