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Acoustic echo and background noise can seriously degrade the intelligibilityof speech. In practice, echo and noise suppression are usually treated as twoseparated 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), tojointly perform acoustic echo cancellation and noise suppression with the helpof adaptive filters. The MC-TCN cascades two separation cores, which are usedto extract robust magnitude spectra feature and to enhance magnitude and phasesimultaneously. Experimental results reveal that the proposed method canachieve superior performance by removing both echo and noise in real-time. Interms of DECMOS, the subjective test shows our method achieves a mean score of4.41 and outperforms the INTERSPEECH2021 AEC-Challenge baseline by 0.54.

Joint Echo Cancellation and Noise Suppression based on Cascaded Magnitude and Complex Mask Estimation