A STOCHASTIC SINUSOIDAL MODEL WITH APPLICATION TO SPEECH AND EEG-SLEEP SPINDLE SIGNALS

Abstract : In this paper, we propose to investigate stochastic sinusoidal models in order to characterise quasi-periodic signals. Indeed, in comparison to the broadly used autoregressive (AR) models, a sinusoidal approach seems to be more efficient to capture quasi-periodic feature. Using AR process as a model for the sine wave magnitudes makes it possible to track the frequential non-stationarity of the signal. The scheme we propose operates as follows: once the frequency components of the signal are obtained, estimating the magnitudes of each sine component of the model is performed by means of an Expectation-Maximisation (EM) algorithm based on Kalman smoothing. Results are provided on sleep spindle and speech
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https://hal.archives-ouvertes.fr/hal-00167735
Contributor : Eric Grivel <>
Submitted on : Wednesday, August 22, 2007 - 3:03:22 PM
Last modification on : Wednesday, January 31, 2018 - 1:46:02 PM

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  • HAL Id : hal-00167735, version 1

Citation

David Labarre, Eric Grivel, Yannick Berthoumieu, Mohamed Najim. A STOCHASTIC SINUSOIDAL MODEL WITH APPLICATION TO SPEECH AND EEG-SLEEP SPINDLE SIGNALS. EUSIPCO, 2002, Toulouse, France. pp. ⟨hal-00167735⟩

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