Weighted Recursive Least Square Filter and Neural Network Based Residual ECHO Suppression for the AEC-Challenge

Ziteng Wang; Yueyue Na; Zhang Liu; Biao Tian; Qiang Fu

ICASSP 2021accepted0 citations

Weighted Recursive Least Square Filter and Neural Network Based Residual ECHO Suppression for the AEC-Challenge

Ziteng Wang, Yueyue Na, Zhang Liu, Biao Tian, Qiang Fu

Abstract

This paper presents a real-time Acoustic Echo Cancellation (AEC) algorithm submitted to the AEC-Challenge. The algorithm consists of three modules: Generalized Cross-Correlation with PHAse Transform (GCC-PHAT) based time delay compensation, weighted Recursive Least Square (wRLS) based linear adaptive filtering and neural network based residual echo suppression. The wRLS filter is derived from a novel semi-blind source separation perspective. The neural network model predicts a Phase-Sensitive Mask (PSM) based on the aligned reference and the linear filter output. The algorithm achieved a mean subjective score of 4.00 and ranked 2nd in the AEC-Challenge.

BibTeX

@inproceedings{icassp2021_weightedrecursiv,
  title = {Weighted Recursive Least Square Filter and Neural Network Based Residual ECHO Suppression for the AEC-Challenge},
  author = {Ziteng Wang and Yueyue Na and Zhang Liu and Biao Tian and Qiang Fu},
  booktitle = {ICASSP 2021},
  year = {2021}
}