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Acoustic Echo Cancellation (AEC) plays a key role in speech interaction bysuppressing the echo received at microphone introduced by acousticreverberations from loudspeakers. Since the performance of linear adaptivefilter (AF) would degrade severely due to nonlinear distortions, backgroundnoises, and microphone clipping in real scenarios, deep learning has beenemployed for AEC for its good nonlinear modelling ability. In this paper, weconstructed an end-to-end multi-scale attention neural network for AEC.Temporal convolution is first used to transform waveform into spectrogram. Thespectrograms of the far-end reference and the near-end mixture areconcatenated, and fed to a temporal convolution network (TCN) with stackeddilated convolution layers. Attention mechanism is performed among theserepresentations from different layers to adaptively extract relevant featuresby referring to the previous hidden state in the encoder long short-term memory(LSTM) unit. The representations are weighted averaged and fed to the encoderLSTM for the near-end speech estimation. Experiments show the superiority ofour method in terms of the echo return loss enhancement (ERLE) for single-talkperiods and the perceptual evaluation of speech quality (PESQ) score fordouble-talk periods in background noise and nonlinear distortion scenarios.

Multi-Scale Attention Neural Network for Acoustic Echo Cancellation