Name
Title | ||
|---|---|---|
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. |
Year | Venue | Keywords |
|---|---|---|
2002 | Toulouse | kalman filters,autoregressive processes,electroencephalography,expectation-maximisation algorithm,feature extraction,medical signal processing,sleep,smoothing methods,speech,eeg-sleep spindle signals,kalman smoothing,autoregressive models,frequency components,frequential nonstationarity,quasiperiodic feature capture,quasiperiodic signals,sine wave magnitudes,stochastic sinusoidal model |
Field | DocType | ISSN |
Autoregressive model,Sleep spindle,Pattern recognition,Computer science,Speech recognition,Artificial intelligence,Ar process,Sinusoidal model,Electroencephalography,Sine wave | Conference | 2219-5491 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Labarre | 1 | 2 | 1.75 |
| E. Grivel | 2 | 74 | 8.43 |
| Y. Berthoumieu | 3 | 389 | 51.66 |
| Mohamed Najim | 4 | 149 | 32.29 |