  Hybrid meetings have become increasingly necessary during the post-COVID
period and also brought new challenges for solving audio-related problems. In
particular, the interplay between acoustic echo and acoustic howling in a
hybrid meeting makes the joint suppression of them difficult. This paper
proposes a deep learning approach to tackle this problem by formulating a
recurrent feedback suppression process as an instantaneous speech separation
task using the teacher-forced training strategy. Specifically, a self-attentive
recurrent neural network is utilized to extract the target speech from
microphone recordings with accessible and learned reference signals, thus
suppressing acoustic echo and acoustic howling simultaneously. Different
combinations of input signals and loss functions have been investigated for
performance improvement. Experimental results demonstrate the effectiveness of
the proposed method for suppressing echo and howling jointly in hybrid
meetings.
