Deep Residual Echo Suppression with A Tunable Tradeoff Between Signal Distortion and Echo Suppression
Amir Ivry,
Israel Cohen,
Baruch Berdugo
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 … Show more
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