Normalized Multichannel Frequency-Domain LMS Filter With Nearest Kronecker Product Decomposition for Blind Identification of Low-Rank Acoustic Systems

Zhimin Qiu, Hongsen He, Jingdong Chen, Jacob Benesty, Yi Yu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper proposes a multichannel frequency-domain adaptive filtering algorithm to blindly identify low-rank acoustic systems. The model filters of the multichannel acoustic impulse responses are decomposed into two sets of short sub-filters through the nearest Kronecker product (NKP). An extended multichannel frequency-domain signal model and its associated cost function are established by using these short sub-filters. The normalized multichannel frequency-domain least-mean-square (NMCFLMS) algorithm based on NKP is subsequently derived according to the Newton’s iteration criterion. Simulations show that the proposed algorithm is computationally more efficient and has a better convergence behavior for blindly identifying multichannel acoustic systems than the conventional NMCFLMS adaptive algorithm, regardless of whether the excitation is a white sequence or a speech signal.

Original languageEnglish
Title of host publication32nd European Signal Processing Conference, EUSIPCO 2024 - Proceedings
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages226-230
Number of pages5
ISBN (Electronic)9789464593617
DOIs
StatePublished - 2024
Event32nd European Signal Processing Conference, EUSIPCO 2024 - Lyon, France
Duration: 26 Aug 202430 Aug 2024

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Conference

Conference32nd European Signal Processing Conference, EUSIPCO 2024
Country/TerritoryFrance
CityLyon
Period26/08/2430/08/24

Keywords

  • Blind system identification
  • frequency-domain adaptive filter
  • multichannel acoustic systems
  • nearest Kronecker product decomposition

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