Name
Abstract | ||
|---|---|---|
A macrotile estimation algorithm is introduced to estimate the covariance of locally stationary processes. A macrotile algorithm uses a penalized method to optimize the partition of the space in orthogonal subspaces, and the estimation is computed with a projection operator. It is implemented by searching for a best basis among a dictionary of orthogonal bases and by constructing an adaptive segmentation of this basis to estimate the covariance coefficients. The macrotile algorithm provides a consistent estimation of the covariance of locally stationary processes, using a dictionary of local cosine bases. This estimation is computed with a fast algorithm. Macrotile algorithms apply to other estimation problems such as the removal of additive noise in signals. This simpler problem is used as an intuitive guide to better understand the case of covariance estimation. Examples of removal of white noise from sounds illustrate the results. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/TSP.2002.808116 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
estimation problem,signal estimation,covariance estimation,consistent estimation,covariance coefficient,stationary process,spectrum estimation.,macrotile estimation algorithm,macrotile algorithm,best basis,fast algorithm,noise removal,local stationarity,index terms—best basis,additive noise,stationary covariance,cosine basis,indexing terms,signal processing,white noise,consistent estimator,spectrum,covariance matrix,projection operator,dictionaries,estimation theory,estimation,acoustic noise | Mathematical optimization,Covariance function,Estimation of covariance matrices,Rational quadratic covariance function,Covariance intersection,White noise,Estimation theory,Matérn covariance function,Mathematics,Covariance | Journal |
Volume | Issue | ISSN |
51 | 3 | 1053-587X |
Citations | PageRank | References |
6 | 0.65 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| D. L. Donoho | 1 | 9350 | 1189.81 |
| Stéphane Mallat | 2 | 4107 | 718.30 |
| R. von Sachs | 3 | 6 | 0.65 |
| Y. Samuelides | 4 | 6 | 0.65 |