344 citations · 1.2k across the 37 of their papers we have counts for
4 papers · 2 filters
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…
Expectation-Maximization for Speech Source Separation Using Convolutive Transfer Function
Xiaofei Li, Laurent Girin, Radu Horaud
This paper addresses the problem of under-determinded speech source separation from multichannel microphone singals, i.e. the convolutive mixtures of multiple sources. The time-dom…
Speech enhancement with variational autoencoders and alpha-stable distributions
Simon Leglaive, Umut Simsekli, Antoine Liutkus +2
This paper focuses on single-channel semi-supervised speech enhancement. We learn a speaker-independent deep generative speech model using the framework of variational autoencoders…
A variance modeling framework based on variational autoencoders for speech enhancement
Simon Leglaive, Laurent Girin, Radu Horaud
In this paper we address the problem of enhancing speech signals in noisy mixtures using a source separation approach. We explore the use of neural networks as an alternative to a…