Nonlinear Residual Echo Suppression Based on Multi-stream Conv-TasNet
TL
TL;DR
Both the residual signal of the linear acoustic echo cancellation system, and the output of the adaptive filter are adopted to form multiple streams for the Conv-TasNet, resulting in more effective echo suppression while keeping a lower latency of the whole system.
Abstract
Acoustic echo cannot be entirely removed by linear adaptive filters due to the nonlinear relationship between the echo and far-end signal. Usually a post processing module is required to further suppress the echo. In this paper, we propose a residual echo suppression method based on the modification of fully convolutional time-domain audio separation network (Conv-TasNet). Both the residual signal of the linear acoustic echo cancellation system, and the output of the adaptive filter are adopted to form multiple streams for the Conv-TasNet, resulting in more effective echo suppression while keeping a lower latency of the whole system. Simulation results validate the efficacy of the proposed method in both single-talk and double-talk situations.
References30 items
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Deep Learning for Joint Acoustic Echo and Noise Cancellation with Nonlinear Distortions
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Conv-TasNet: Surpassing Ideal Time–Frequency Magnitude Masking for Speech Separation
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Deep Learning for Acoustic Echo Cancellation in Noisy and Double-Talk Scenarios
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