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.
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Communication dans un congrès
42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017) , Mar 2017, La Nouvelle Orléans, LA, United States. Proc. in ICASSP. <http://www.ieee-icassp2017.org/>
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https://hal.archives-ouvertes.fr/hal-01448603
Contributeur : Tom Dupré La Tour <>
Soumis le : mardi 21 mars 2017 - 11:51:36
Dernière modification le : samedi 25 mars 2017 - 01:10:26

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  • HAL Id : hal-01448603, version 2

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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. Proc. in ICASSP. <http://www.ieee-icassp2017.org/>. <hal-01448603v2>

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