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

Citation Author(s):
Amir Ivry, Israel Cohen, Baruch Berdugo
Submitted by:
Amir Ivry
Last updated:
22 June 2021 - 4:19am
Document Type:
Presentation Slides
Document Year:
Amir Ivry
Paper Code:

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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