Weighted Recursive Least Square Filter and Neural Network based Residual Echo Suppression for the AEC-Challenge
Sound
2021-02-19 v2 Audio and Speech Processing
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.
Keywords
Cite
@article{arxiv.2102.08551,
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},
journal= {arXiv preprint arXiv:2102.08551},
year = {2021}
}
Comments
5 pages, 2 figures, accepted by ICASSP 2021