Speaker: Guillaume Carbajal
Date: April 12 2018
Abstract:
A residual echo suppressor (RES) aims to suppress the residual echo in the output of an acoustic echo canceler (AEC).
Spectral-based RES approaches typically estimate the magnitude spectra of the near-end speech and the residual echo from a single input, that is either the far-end speech or the echo computed by the AEC,
and derive the RES filter coefficients accordingly.
These single inputs do not always suffice to discriminate the near-end speech from the remaining echo.
We propose a neural network-based approach that directly estimates the RES filter coefficients from multiple inputs, including the AEC output, the far-end speech, and/or the echo computed by the AEC.
We evaluate our system on real recordings of acoustic echo and near-end speech acquired in various situations with a smart speaker.
We compare it to two single-input spectral-based approaches in terms of echo reduction and near-end speech distortion.
