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

F-T-LSTM based Complex Network for Joint Acoustic Echo Cancellation and Speech Enhancement