Tayloraecnet: A Taylor Style Neural Network For Full-Band Echo Cancellation

Weiming Xu; Zhihao Guo

ICASSP 2023accepted0 citations

Tayloraecnet: A Taylor Style Neural Network For Full-Band Echo Cancellation

Abstract

This paper describes aecX team’s entry to the ICASSP 2023 acoustic echo cancellation (AEC) challenge. Our system consists of an adaptive filter and a proposed full-band Taylor-style acoustic echo cancellation neural network (TaylorAECNet) as a post-filter. Specifically, we leverage the recent advances in Taylor expansion based decoupling-style interpretable speech enhancement [1] and explore its feasibility in the AEC task. Our TaylorAECNet based approach achieves an overall mean opinion score (MOS) of 4.241, a word accuracy (WAcc) ratio of 0.767, and ranks 5th in the non-personalized track (track 1).

BibTeX

@inproceedings{icassp2023_tayloraecnetatay,
  title = {Tayloraecnet: A Taylor Style Neural Network For Full-Band Echo Cancellation},
  author = {Weiming Xu and Zhihao Guo},
  booktitle = {ICASSP 2023},
  year = {2023}
}