TY - GEN
T1 - General Robust Subband Adaptive Filtering for Echo Cancellation
AU - Yu, Yi
AU - He, Hongsen
AU - De Lamare, Rodrigo C.
AU - Chen, Badong
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In this paper, we propose a general robust subband adaptive filtering (GR-SAF) framework against impulsive noise by minimizing the mean square deviation under the random-walk model with individual weight uncertainty. Specifically, by choosing different scaling factors from some robust criteria in the GR-SAF scheme, we can easily obtain different GR-SAF algorithms. Simulations in the echo cancellation have verified that the proposed GR-SAF algorithm out-performs its counterparts.
AB - In this paper, we propose a general robust subband adaptive filtering (GR-SAF) framework against impulsive noise by minimizing the mean square deviation under the random-walk model with individual weight uncertainty. Specifically, by choosing different scaling factors from some robust criteria in the GR-SAF scheme, we can easily obtain different GR-SAF algorithms. Simulations in the echo cancellation have verified that the proposed GR-SAF algorithm out-performs its counterparts.
KW - Echo cancellation
KW - impulsive noise
KW - mean square deviation
KW - subband adaptive filter
UR - https://www.scopus.com/pages/publications/85142724382
U2 - 10.1109/MLSP55214.2022.9943313
DO - 10.1109/MLSP55214.2022.9943313
M3 - 会议稿件
AN - SCOPUS:85142724382
T3 - IEEE International Workshop on Machine Learning for Signal Processing, MLSP
BT - 2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing, MLSP 2022
PB - IEEE Computer Society
T2 - 32nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2022
Y2 - 22 August 2022 through 25 August 2022
ER -