Deep Residual Echo Suppression with A Tunable Tradeoff Between Signal Distortion and Echo Suppression

• URL: http://arxiv.org/abs/2106.13531v1
• Date: Fri, 25 Jun 2021 09:49:18 GMT
• Title: Deep Residual Echo Suppression with A Tunable Tradeoff Between Signal Distortion and Echo Suppression
• Authors: Amir Ivry, Israel Cohen, Baruch Berdugo
• Abstract summary: A UNet neural network maps the outputs of a linear acoustic echo canceler to the desired signal in the spectral domain. The system employs 136 thousand parameters, and requires 1.6 Giga floating-point operations per second and 10 Mega-bytes of memory.
• Score: 13.558688470594676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a residual echo suppression method using a UNet neural network that directly maps the outputs of a linear acoustic echo canceler to the desired signal in the spectral domain. This system embeds a design parameter that allows a tunable tradeoff between the desired-signal distortion and residual echo suppression in double-talk scenarios. The system employs 136 thousand parameters, and requires 1.6 Giga floating-point operations per second and 10 Mega-bytes of memory. The implementation satisfies both the timing requirements of the AEC challenge and the computational and memory limitations of on-device applications. Experiments are conducted with 161~h of data from the AEC challenge database and from real independent recordings. We demonstrate the performance of the proposed system in real-life conditions and compare it with two competing methods regarding echo suppression and desired-signal distortion, generalization to various environments, and robustness to high echo levels.

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