Multi-Scale Attention Neural Network for Acoustic Echo Cancellation

TL;DR

This paper introduces a multi-scale attention neural network for acoustic echo cancellation that effectively models nonlinear distortions and noise, outperforming traditional adaptive filters in various challenging scenarios.

Contribution

It presents a novel end-to-end neural network architecture combining temporal convolution, attention mechanisms, and LSTM for improved AEC performance.

Findings

Outperforms traditional methods in echo suppression (ERLE)

Achieves higher speech quality (PESQ) scores in noisy conditions

Effective in nonlinear distortion scenarios

Abstract

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.