Name
Abstract | ||
|---|---|---|
Two proportionate affine projection sign algorithms (APSAs) are proposed for network echo cancellation (NEC) applications where the impulse response is often real-valued with sparse coefficients and long filter length. The proposed proportionate-type algorithms can achieve fast convergence and low steady-state misalignment by adopting a proportionate regularization matrix to the APSA. Benefiting from the characteristics of l1-norm optimization, affine projection, and proportionate matrix, the new algorithms are more robust to impulsive interferences and colored input than the proportionate least mean squares (PNLMS) algorithm and the robust proportionate affine projection algorithm (Robust PAPA). The new algorithms also achieve much faster convergence rate in sparse impulse responses than the original APSA and the normalized sign algorithm (NSA). The new algorithms are robust to all types of NEC impulse response with different sparseness without the need to change parameters or estimate the sparseness of the impulse response. The computational complexity of the new algorithms is lower than the affine projection algorithm (APA) family due to the elimination of the matrix inversion. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TASL.2011.2125955 | IEEE Transactions on Audio, Speech & Language Processing |
Keywords | Field | DocType |
proportionate matrix,nec impulse response,proportionate affine projection sign,network echo cancellation,robust proportionate affine projection,affine projection algorithm,affine projection,new algorithm,normalized sign algorithm,proportionate regularization matrix,impulse response,least mean square,computational complexity,robustness,convergence rate,transient response,steady state,sparse matrices,adaptive filter,convergence,interference,speech,adaptive filters | Impulse response,Computer science,Impulse (physics),Robustness (computer science),Rate of convergence,Adaptive filter,Artificial intelligence,Sparse matrix,Least mean squares filter,Pattern recognition,Algorithm,Speech recognition,Computational complexity theory | Journal |
Volume | Issue | ISSN |
19 | 8 | 1558-7916 |
Citations | PageRank | References |
32 | 1.34 | 23 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zengli Yang | 1 | 43 | 3.40 |
| Y. R. Zheng | 2 | 177 | 7.02 |
| S. L. Grant | 3 | 32 | 1.34 |