TY - GEN

T1 - On whitening for Krylov-proportionate normalized least-mean-square algorithm

AU - Yukawa, Masahiro

PY - 2008

Y1 - 2008

N2 - The contributions of this paper are twofold. The first is to give theoretical motivation for whitening in the recently proposed adaptive filtering algorithm named the Krylov-proportionate normalized least-mean-square (KPNLMS) algorithm. The second is to present the details of whitening in KPNLMS (In the original work of KPNLMS, the whitening procedure is mentioned but is not described in detail). An interesting connection among the transform-domain adaptive filter (TDAF), proportionate normalized least-mean-square (PNLMS), and KPNLMS algorithms is also provided. Numerical examples demonstrate that KPNLMS drastically outperforms TDAF especially in noisy situations.

AB - The contributions of this paper are twofold. The first is to give theoretical motivation for whitening in the recently proposed adaptive filtering algorithm named the Krylov-proportionate normalized least-mean-square (KPNLMS) algorithm. The second is to present the details of whitening in KPNLMS (In the original work of KPNLMS, the whitening procedure is mentioned but is not described in detail). An interesting connection among the transform-domain adaptive filter (TDAF), proportionate normalized least-mean-square (PNLMS), and KPNLMS algorithms is also provided. Numerical examples demonstrate that KPNLMS drastically outperforms TDAF especially in noisy situations.

KW - Adaptive filter

KW - Krylov subspace

KW - Proportionate NLMS

KW - Whitening

UR - http://www.scopus.com/inward/record.url?scp=58049189342&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=58049189342&partnerID=8YFLogxK

U2 - 10.1109/MLSP.2008.4685499

DO - 10.1109/MLSP.2008.4685499

M3 - Conference contribution

AN - SCOPUS:58049189342

SN - 9781424423767

T3 - Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

SP - 315

EP - 320

BT - Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

T2 - 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

Y2 - 16 October 2008 through 19 October 2008

ER -