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Multiple-input neural network-based residual echo suppression
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- Citation Author(s):
- 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:
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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.