Multi-Scale Attention Neural Network for Acoustic Echo Cancellation
This work addresses echo suppression in real-world speech systems, offering incremental improvements for applications like teleconferencing and voice assistants.
The paper tackled acoustic echo cancellation in speech interaction by proposing a multi-scale attention neural network, achieving superior performance with improved echo return loss enhancement (ERLE) for single-talk periods and perceptual evaluation of speech quality (PESQ) scores for double-talk periods in noisy and nonlinear scenarios.
Acoustic Echo Cancellation (AEC) plays a key role in speech interaction by suppressing the echo received at microphone introduced by acoustic reverberations from loudspeakers. Since the performance of linear adaptive filter (AF) would degrade severely due to nonlinear distortions, background noises, and microphone clipping in real scenarios, deep learning has been employed for AEC for its good nonlinear modelling ability. In this paper, we constructed an end-to-end multi-scale attention neural network for AEC. Temporal convolution is first used to transform waveform into spectrogram. The spectrograms of the far-end reference and the near-end mixture are concatenated, and fed to a temporal convolution network (TCN) with stacked dilated convolution layers. Attention mechanism is performed among these representations from different layers to adaptively extract relevant features by referring to the previous hidden state in the encoder long short-term memory (LSTM) unit. The representations are weighted averaged and fed to the encoder LSTM for the near-end speech estimation. Experiments show the superiority of our method in terms of the echo return loss enhancement (ERLE) for single-talk periods and the perceptual evaluation of speech quality (PESQ) score for double-talk periods in background noise and nonlinear distortion scenarios.