Acoustic Echo Cancellation and Noise Suppression with a Full Time-Frequency Cascaded Neural Network
Ning Sun, Hongqing Liu, Hao Li, Yi Zhou, Lu Gan · 2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP) · 2022
With the developments of various multi-function communication services, acoustic echoes and background noises inevitably appear in hands-free calling occasions. Different from using the combination of neural network and traditional acoustic echo cancellation (AEC) method, this paper directly proposes a time-frequency complex cascaded neural network (TFCN) for echo cancellation and noise suppression. To that aim, in frequency domain, complex LSTM layers are employed to process the real and imaginary signals. After that, an end-to-end time domain network is designed using dilated convolution layers to further remove residual interferences. By adding rich delay information to the dataset and optimizing the model by a weighted loss function, the generalization ability of the model is also improved. The extensive experimental results show that the proposed frame-work is robust to blind test datasets, effectively removes echoes and noises, and achieves an excellent performance on AECMOS scores. The subjective mean score of the proposed method is 4.37, which is 0.50 higher than the INTERSPEECH2021 AEC-Challenge baseline.