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Multiple-input neural network-based residual echo suppression

Citation Author(s):
Guillaume Carbajal, Romain Serizel, Emmanuel Vincent, Eric Humbert
Submitted by:
Guillaume Carbajal
Last updated:
25 April 2018 - 5:13am
Document Type:
Poster
Document Year:
2018
Event:
Presenters:
Guillaume CARBAJAL
Paper Code:
AASP-P1.5
Categories:
 

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. In this paper, 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.

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