Name
Abstract | ||
|---|---|---|
In this work we explore the capabilities of two noise-assisted EMD methods: Ensemble EMD (EEMD) and the recently proposed Complete Ensemble EMD with Adaptive Noise (CEEMDAN), to recover a pure tone embedded in different kinds of noise, both stationary and nonstationary. Experiments are carried out for assessing their performances with respect to the level of the added noise and the number of realizations used for averaging. The obtained results partly support empirical recommendations reported in the literature while evidencing new distinctive features. While EEMD presents quite different behaviors for different situations, CEEMDAN evidences some robustness with an almost unaffected performance for the studied cases. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1142/S1793536912500252 | ADVANCES IN DATA SCIENCE AND ADAPTIVE ANALYSIS |
Keywords | Field | DocType |
Empirical mode decomposition (EMD), noise-assisted data analysis (NADA) | Pattern recognition,Pure tone,Speech recognition,Robustness (computer science),Artificial intelligence,Mathematics | Journal |
Volume | Issue | ISSN |
4 | 4 | 2424-922X |
Citations | PageRank | References |
15 | 1.06 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcelo A. Colominas | 1 | 161 | 13.50 |
| Gastón Schlotthauer | 2 | 180 | 15.59 |
| María Eugenia Torres | 3 | 183 | 12.23 |
| Patrick Flandrin | 4 | 2307 | 568.82 |