Name
Title | ||
|---|---|---|
ROBUST RECURSIVE LEAST M-ESTIMATE ADAPTIVE FILTER FOR THE IDENTIFICATION OF LOW-RANK ACOUSTIC SYSTEMS |
Abstract | ||
|---|---|---|
To identify acoustic systems (which are low-rank in nature) in non-Gaussian and Gaussian noise, a robust recursive least M-estimate adaptive filtering algorithm is developed in this paper by applying the nearest Kronecker product to decompose the acoustic impulse response. Two M-estimators, i.e., the Cauchy and Welsch estimators, are employed to define the cost function of the adaptive filter, leading to a class of numerically stable adaptive filtering algorithms, which are robust to non-Gaussian noise. The effectiveness of the developed algorithm is validated in acoustic environments with both Gaussian and non-Gaussian noise. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICASSP39728.2021.9413983 | 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) |
Keywords | DocType | Citations |
Acoustic system identification, adaptive filter, low-rank system, nearest Kronecker product, recursive least M-estimate, robustness | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongsen He | 1 | 23 | 4.89 |
| Jingdong Chen | 2 | 1460 | 128.79 |
| Jacob Benesty | 3 | 1386 | 136.42 |
| Yi Yu | 4 | 194 | 17.90 |