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.
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01448603
Contributor : Tom Dupré La Tour <>
Submitted on : Tuesday, March 21, 2017 - 11:51:36 AM
Last modification on : Wednesday, February 20, 2019 - 1:28:33 AM

File

duprelatour2017.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

  • HAL Id : hal-01448603, version 2

Citation

Tom Dupré La Tour, Yves Grenier, Alexandre Gramfort. Parametric estimation of spectrum driven by an exogenous signal. 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017) , Mar 2017, La Nouvelle Orléans, LA, United States. ⟨hal-01448603v2⟩

Share

Metrics

Record views

424

Files downloads

294