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

Masahiro Yukawa

doi:10.1109/MLSP.2008.4685499

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

Masahiro Yukawa

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

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.

本文言語	English
ホスト出版物のタイトル	Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
ページ	315-320
ページ数	6
DOI	https://doi.org/10.1109/MLSP.2008.4685499
出版ステータス	Published - 2008
外部発表	はい
イベント	2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 - Cancun, Mexico 継続期間: 2008 10月 16 → 2008 10月 19

出版物シリーズ

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

Other

Other	2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
国/地域	Mexico
City	Cancun
Period	08/10/16 → 08/10/19

ASJC Scopus subject areas

人工知能
ソフトウェア
電子工学および電気工学

文献へのアクセス

10.1109/MLSP.2008.4685499

他のファイルとリンク

引用スタイル

Yukawa, M 2008, On whitening for Krylov-proportionate normalized least-mean-square algorithm. in Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008., 4685499, Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008, pp. 315-320, 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008, Cancun, Mexico, 08/10/16. https://doi.org/10.1109/MLSP.2008.4685499

@inproceedings{d50627fa50d442faae4a5e5815563a99,

title = "On whitening for Krylov-proportionate normalized least-mean-square algorithm",

abstract = "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.",

keywords = "Adaptive filter, Krylov subspace, Proportionate NLMS, Whitening",

author = "Masahiro Yukawa",

year = "2008",

doi = "10.1109/MLSP.2008.4685499",

language = "English",

isbn = "9781424423767",

series = "Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008",

pages = "315--320",

booktitle = "Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008",

note = "2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 ; Conference date: 16-10-2008 Through 19-10-2008",

}

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 -

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

抄録

出版物シリーズ

Other

ASJC Scopus subject areas

文献へのアクセス

他のファイルとリンク

フィンガープリント

引用スタイル