  With the increasing demand for audio communication and online conference,
ensuring the robustness of Acoustic Echo Cancellation (AEC) under the
complicated acoustic scenario including noise, reverberation and nonlinear
distortion has become a top issue. Although there have been some traditional
methods that consider nonlinear distortion, they are still inefficient for echo
suppression and the performance will be attenuated when noise is present. In
this paper, we present a real-time AEC approach using complex neural network to
better modeling the important phase information and frequency-time-LSTMs
(F-T-LSTM), which scan both frequency and time axis, for better temporal
modeling. Moreover, we utilize modified SI-SNR as cost function to make the
model to have better echo cancellation and noise suppression (NS) performance.
With only 1.4M parameters, the proposed approach outperforms the AEC-challenge
baseline by 0.27 in terms of Mean Opinion Score (MOS).
