169 citations · 528 across the 23 of their papers we have counts for
5 papers · 1 filter
A Recurrent Variational Autoencoder for Speech Enhancement
Simon Leglaive, Xavier Alameda-Pineda, Laurent Girin +1
This paper presents a generative approach to speech enhancement based on a recurrent variational autoencoder (RVAE). The deep generative speech model is trained using clean speech…
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…
Audio-noise Power Spectral Density Estimation Using Long Short-term Memory
Xiaofei Li, Simon Leglaive, Laurent Girin +1
We propose a method using a long short-term memory (LSTM) network to estimate the noise power spectral density (PSD) of single-channel audio signals represented in the short time F…
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…