82 citations · 167 across the 16 of their papers we have counts for
18 papers
The CHiME-7 UDASE task: Unsupervised domain adaptation for conversational speech enhancement
Simon Leglaive, Léonie Borne, Efthymios Tzinis +6
Supervised speech enhancement models are trained using artificially generated mixtures of clean speech and noise signals, which may not match real-world recording conditions at tes…
Fast and efficient speech enhancement with variational autoencoders
Mostafa Sadeghi, Romain Serizel
Unsupervised speech enhancement based on variational autoencoders has shown promising performance compared with the commonly used supervised methods. This approach involves the use…
Audio-visual speech enhancement with a deep Kalman filter generative model
Ali Golmakani, Mostafa Sadeghi, Romain Serizel
Deep latent variable generative models based on variational autoencoder (VAE) have shown promising performance for audiovisual speech enhancement (AVSE). The underlying idea is to…
A weighted-variance variational autoencoder model for speech enhancement
Ali Golmakani, Mostafa Sadeghi, Xavier Alameda-Pineda +1
We address speech enhancement based on variational autoencoders, which involves learning a speech prior distribution in the time-frequency (TF) domain. A zero-mean complex-valued G…
Expression-preserving face frontalization improves visually assisted speech processing
Zhiqi Kang, Mostafa Sadeghi, Radu Horaud +1
Face frontalization consists of synthesizing a frontally-viewed face from an arbitrarily-viewed one. The main contribution of this paper is a frontalization methodology that preser…
A Sparsity-promoting Dictionary Model for Variational Autoencoders
Mostafa Sadeghi, Paul Magron
Structuring the latent space in probabilistic deep generative models, e.g., variational autoencoders (VAEs), is important to yield more expressive models and interpretable represen…