Name
Abstract | ||
|---|---|---|
Abstract—The aim of a variable step size normalized least- mean-square (VSS-NLMS) algorithm is to try to solve the con- flicting requirement of fast convergence and low misadjustment of the NLMS algorithm. Numerous VSS-NLMS algorithms can be found in the literature with a common point for most of them: they may not work very reliably since they depend on several parameters that are not simple to tune in practice. The objective of this letter is twofold. First, we explain a simple and elegant way to derive VSS-NLMS-type algorithms. Second, a new nonparametric VSS-NLMS is proposed that is easy to control and gives good performances in the context of acoustic echo cancellation. Index Terms—Acoustic echo cancellation, adaptive filters, least |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/LSP.2006.876323 | Signal Processing Letters, IEEE |
Keywords | Field | DocType |
acoustic signal processing,convergence of numerical methods,echo,echo suppression,least mean squares methods,acoustic echo cancellation,fast convergence,nonparametric VSS NLMS algorithm,normalized least-mean-square,variable step size,Acoustic echo cancellation,adaptive filters,least mean square (LMS),normalized LMS (NLMS),variable step size NLMS | Convergence (routing),Mathematical optimization,Normalization (statistics),Pattern recognition,Algorithm,Nonparametric statistics,Artificial intelligence,Adaptive filter,Mathematics | Journal |
Volume | Issue | ISSN |
13 | 10 | 1070-9908 |
Citations | PageRank | References |
127 | 7.43 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jacob Benesty | 1 | 1386 | 136.42 |
| H. Rey | 2 | 274 | 18.90 |
| Leonardo Rey Vega | 3 | 135 | 9.54 |
| S. Tressens | 4 | 269 | 18.38 |