TY - GEN
T1 - An Exploration of Task-Decoupling on Two-Stage Neural Post Filter for Real-Time Personalized Acoustic Echo Cancellation
AU - Zhang, Zihan
AU - Sun, Jiayao
AU - Xia, Xianjun
AU - Wang, Ziqian
AU - Yan, Xiaopeng
AU - Xiao, Yijian
AU - Xie, Lei
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Deep learning based techniques have been popularly adopted in acoustic echo cancellation (AEC). Utilization of speaker representation has extended the frontier of AEC, thus attracting many researchers' interest in personalized acoustic echo cancellation (PAEC). Meanwhile, task-decoupling strategies are widely adopted in speech enhancement. To further explore the task-decoupling approach, we propose to use a two-stage task-decoupling post-filter (TDPF) in PAEC. Furthermore, a multi-scale local-global speaker representation is applied to improve speaker extraction in PAEC. Experimental results indicate that the task-decoupling model can yield better performance than a single joint network. The optimal approach is to decouple the echo cancellation from noise and interference speech suppression. Based on the task-decoupling sequence, optimal training strategies for the two-stage model are explored afterwards.
AB - Deep learning based techniques have been popularly adopted in acoustic echo cancellation (AEC). Utilization of speaker representation has extended the frontier of AEC, thus attracting many researchers' interest in personalized acoustic echo cancellation (PAEC). Meanwhile, task-decoupling strategies are widely adopted in speech enhancement. To further explore the task-decoupling approach, we propose to use a two-stage task-decoupling post-filter (TDPF) in PAEC. Furthermore, a multi-scale local-global speaker representation is applied to improve speaker extraction in PAEC. Experimental results indicate that the task-decoupling model can yield better performance than a single joint network. The optimal approach is to decouple the echo cancellation from noise and interference speech suppression. Based on the task-decoupling sequence, optimal training strategies for the two-stage model are explored afterwards.
KW - personalized acoustic echo cancellation
KW - speaker representation
KW - task-decoupling
KW - two-stage
UR - https://www.scopus.com/pages/publications/85184657027
U2 - 10.1109/ASRU57964.2023.10389770
DO - 10.1109/ASRU57964.2023.10389770
M3 - 会议稿件
AN - SCOPUS:85184657027
T3 - 2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023
BT - 2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023
Y2 - 16 December 2023 through 20 December 2023
ER -