82 citations · 167 across the 16 of their papers we have counts for
4 papers · 1 filter
Mixture of Inference Networks for VAE-based Audio-visual Speech Enhancement
Mostafa Sadeghi, Xavier Alameda-Pineda
In this paper, we are interested in unsupervised (unknown noise) audio-visual speech enhancement based on variational autoencoders (VAEs), where the probability distribution of cle…
Robust Unsupervised Audio-visual Speech Enhancement Using a Mixture of Variational Autoencoders
Mostafa Sadeghi, Xavier Alameda-Pineda
Recently, an audio-visual speech generative model based on variational autoencoder (VAE) has been proposed, which is combined with a nonnegative matrix factorization (NMF) model fo…
Audio-visual Speech Enhancement Using Conditional Variational Auto-Encoders
Mostafa Sadeghi, Simon Leglaive, Xavier Alameda-PIneda +2
Variational auto-encoders (VAEs) are deep generative latent variable models that can be used for learning the distribution of complex data. VAEs have been successfully used to lear…
SSFN -- Self Size-estimating Feed-forward Network with Low Complexity, Limited Need for Human Intervention, and Consistent Behaviour across Trials
Saikat Chatterjee, Alireza M. Javid, Mostafa Sadeghi +4
We design a self size-estimating feed-forward network (SSFN) using a joint optimization approach for estimation of number of layers, number of nodes and learning of weight matrices…