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Guillaume Carbajal

3 accepted papers

2021

Guided Variational Autoencoder for Speech Enhancement with a Supervised Classifier

ICASSP 2021accepted

Recently, variational autoencoders have been successfully used to learn a probabilistic prior over speech signals, which is then used to perform speech enhancement. However, variational autoencoders are trained on clean speech only, which results in a limited ability of extracting the speech signal…

Cited by 0SourceScholar
2021

Variational Autoencoder for Speech Enhancement with a Noise-Aware Encoder

ICASSP 2021accepted

Recently, a generative variational autoencoder (VAE) has been proposed for speech enhancement to model speech statistics. However, this approach only uses clean speech in the training phase, making the estimation particularly sensitive to noise presence, especially in low signal-to-noise ratios (SNR…

Cited by 0SourceScholar
2018

Multiple-Input Neural Network-Based Residual Echo Suppression

ICASSP 2018accepted

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…

Cited by 0SourceScholar