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Deep learning based techniques have been popularly adopted in acoustic echocancellation (AEC). Utilization of speaker representation has extended thefrontier of AEC, thus attracting many researchers' interest in personalizedacoustic echo cancellation (PAEC). Meanwhile, task-decoupling strategies arewidely adopted in speech enhancement. To further explore the task-decouplingapproach, we propose to use a two-stage task-decoupling post-filter (TDPF) inPAEC. Furthermore, a multi-scale local-global speaker representation is appliedto improve speaker extraction in PAEC. Experimental results indicate that thetask-decoupling model can yield better performance than a single joint network.The optimal approach is to decouple the echo cancellation from noise andinterference speech suppression. Based on the task-decoupling sequence, optimaltraining strategies for the two-stage model are explored afterwards.

An Exploration of Task-decoupling on Two-stage Neural Post Filter for Real-time Personalized Acoustic Echo Cancellation