A Frequency-Domain Recursive Least-Squares Adaptive Filtering Algorithm Based On A Kronecker Product Decomposition

Hongsen He; Jingdong Chen; Jacob Benesty; Yi Yu

ICASSP 2023accepted0 citations

A Frequency-Domain Recursive Least-Squares Adaptive Filtering Algorithm Based On A Kronecker Product Decomposition

Hongsen He, Jingdong Chen, Jacob Benesty, Yi Yu

Abstract

This paper proposes a frequency-domain recursive least-squares (RLS) adaptive filtering algorithm for identifying time-varying acoustic systems in noisy environments. The Kronecker product (KP) is employed to decompose the model filter of the acoustic channel impulse response into two sets of short sub-filters, based on which a generalized frequency-domain signal model and the associated cost function are established. A KP based RLS algorithm is subsequently deduced. In comparison with the conventional frequency-domain RLS adaptive filter, the presented algorithm is not only computationally more efficient, but also has a faster convergence rate for the identification of acoustic systems regardless of whether the excitation is a white sequence or a speech signal.

BibTeX

@inproceedings{icassp2023_afrequencydomain,
  title = {A Frequency-Domain Recursive Least-Squares Adaptive Filtering Algorithm Based On A Kronecker Product Decomposition},
  author = {Hongsen He and Jingdong Chen and Jacob Benesty and Yi Yu},
  booktitle = {ICASSP 2023},
  year = {2023}
}