An efficient adaptive filtering scheme based on combining multiple metrics

Osamu Toda, Masahiro Yukawa, Shigenobu Sasaki, Hisakazu Kikuchi

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


We propose a novel adaptive filtering scheme named metric-combining normalized least mean square (MC-NLMS). The proposed scheme is based on iterative metric projections with a metric designed by combining multiple metric-matrices convexly in an adaptive manner, thereby taking advantages of the metrics which rely on multiple pieces of information. We compare the improved PNLMS (IPNLMS) algorithm with the natural proportionate NLMS (NPNLMS) algorithm, which is a special case of MC-NLMS, and it is shown that the performance of NPNLMS is controllable with the combination coefficient as opposed to IPNLMS. We also present an application to an acoustic echo cancellation problem and show the efficacy of the proposed scheme.

Original languageEnglish
Pages (from-to)800-808
Number of pages9
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Issue number3
Publication statusPublished - 2014
Externally publishedYes


  • Adaptive filter
  • Metric projection
  • Proportionate NLMS
  • Transform domain adaptive filter

ASJC Scopus subject areas

  • Signal Processing
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering
  • Applied Mathematics


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