  Acoustic echo and background noise can seriously degrade the intelligibility
of speech. In practice, echo and noise suppression are usually treated as two
separated tasks and can be removed with various digital signal processing (DSP)
and deep learning techniques. In this paper, we propose a new cascaded model,
magnitude and complex temporal convolutional neural network (MC-TCN), to
jointly perform acoustic echo cancellation and noise suppression with the help
of adaptive filters. The MC-TCN cascades two separation cores, which are used
to extract robust magnitude spectra feature and to enhance magnitude and phase
simultaneously. Experimental results reveal that the proposed method can
achieve superior performance by removing both echo and noise in real-time. In
terms of DECMOS, the subjective test shows our method achieves a mean score of
4.41 and outperforms the INTERSPEECH2021 AEC-Challenge baseline by 0.54.
