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Paper data
A stochastic sinusoidal model with application to speech and EEG-sleep spindle signals

Labarre David, Equipe Signal et Image, LAP Bordeaux France
Grivel Eric, Equipe Signal et Image, LAP Bordeaux France
Berthoumieu Yannick, Equipe SACT, IXL Bordeaux France
Najim Mohamed, Equipe Signal et Image, LAP Bordeaux France

Page numbers in the proceedings:
Volume III pp 169-172

Biomedical Processing

Paper 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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