Name
Abstract | ||
|---|---|---|
We propose an adaptive procedure to model non-stationary signals using autoregressive systems with time-varying parameters. A non-stationary signal that is representable by a time-varying autoregressive system has parameters which are expandable in terms of a set of basis functions. The parameters can be found by posing a minimum least-squares modeling problem and solving a large set of normal equations. The costly calculations involved in this problem make an adaptive solution quite desirable. Using the parameter expansions, we convert the modeling into a linear prediction problem and solve it adaptively for a given set of basis functions. We apply our procedure in the modeling of a segment of speech and in the estimation of the evolutionary spectrum of a non-stationary signal. |
Year | DOI | Venue |
|---|---|---|
1994 | 10.1109/ICASSP.1994.389861 | ICASSP (4) |
Keywords | Field | DocType |
costly calculation,adaptive time-varying parametric modeling,adaptive procedure,time-varying autoregressive system,linear prediction problem,basis function,autoregressive system,large set,adaptive solution,non-stationary signal,time-varying parameter,adaptive signal processing,linear prediction,parametric model,polynomials,spectrum,parameter estimation,basis functions,least square,speech processing,parametric statistics,signal processing | Autoregressive model,Speech processing,Mathematical optimization,Parametric model,Pattern recognition,Computer science,Linear prediction,Basis function,Artificial intelligence,Adaptive filter,Spectral analysis,Estimation theory | Conference |
ISBN | Citations | PageRank |
0-7803-1775-0 | 1 | 0.53 |
References | Authors | |
1 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| aydin akan | 1 | 164 | 34.61 |
| L. F. Chaparro | 2 | 45 | 11.06 |