Search Results for author: Mostafa Sadeghi

Found 15 papers, 1 papers with code

Expression-preserving face frontalization improves visually assisted speech processing

no code implementations6 Apr 2022 Zhiqi Kang, Mostafa Sadeghi, Radu Horaud, Xavier Alameda-Pineda

The method alternates between the estimation of (i)~the rigid transformation (scale, rotation, and translation) and (ii)~the non-rigid deformation between an arbitrarily-viewed face and a face model.

Face Model Lip Reading +1

A Sparsity-promoting Dictionary Model for Variational Autoencoders

no code implementations29 Mar 2022 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 representations, and to avoid overfitting.

Variational Inference

Switching Variational Auto-Encoders for Noise-Agnostic Audio-visual Speech Enhancement

no code implementations8 Feb 2021 Mostafa Sadeghi, Xavier Alameda-Pineda

Recently, audio-visual speech enhancement has been tackled in the unsupervised settings based on variational auto-encoders (VAEs), where during training only clean data is used to train a generative model for speech, which at test time is combined with a noise model, e. g. nonnegative matrix factorization (NMF), whose parameters are learned without supervision.

Speech Enhancement

Face Frontalization Based on Robustly Fitting a Deformable Shape Model to 3D Landmarks

no code implementations26 Oct 2020 Zhiqi Kang, Mostafa Sadeghi, Radu Horaud

We propose to model inliers and outliers with the generalized Student's t-probability distribution function, a heavy-tailed distribution that is immune to non-Gaussian errors in the data.

Face Alignment Face Model +2

Deep Variational Generative Models for Audio-visual Speech Separation

no code implementations17 Aug 2020 Viet-Nhat Nguyen, Mostafa Sadeghi, Elisa Ricci, Xavier Alameda-Pineda

To better utilize the visual information, the posteriors of the latent variables are inferred from mixed speech (instead of clean speech) as well as the visual data.

Speech Separation

Unsupervised Performance Analysis of 3D Face Alignment

no code implementations14 Apr 2020 Mostafa Sadeghi, Sylvain Guy, Adrien Raison, Xavier Alameda-Pineda, Radu Horaud

We empirically show that the proposed pipeline is neither method-biased nor data-biased, and that it can be used to assess both the performance of 3DFA algorithms and the accuracy of annotations of face datasets.

3D Face Alignment Face Alignment

Mixture of Inference Networks for VAE-based Audio-visual Speech Enhancement

no code implementations23 Dec 2019 Mostafa Sadeghi, Xavier Alameda-Pineda

Two encoder networks input, respectively, audio and visual data, and the posterior of the latent variables is modeled as a mixture of two Gaussian distributions output from each encoder network.

Speech Enhancement Variational Inference

Robust Unsupervised Audio-visual Speech Enhancement Using a Mixture of Variational Autoencoders

no code implementations10 Nov 2019 Mostafa Sadeghi, Xavier Alameda-Pineda

When visual data is clean, speech enhancement with audio-visual VAE shows a better performance than with audio-only VAE, which is trained on audio-only data.

Speech Enhancement

Audio-visual Speech Enhancement Using Conditional Variational Auto-Encoders

no code implementations7 Aug 2019 Mostafa Sadeghi, Simon Leglaive, Xavier Alameda-Pineda, Laurent Girin, Radu Horaud

Variational auto-encoders (VAEs) are deep generative latent variable models that can be used for learning the distribution of complex data.

Speech Enhancement

SSFN -- Self Size-estimating Feed-forward Network with Low Complexity, Limited Need for Human Intervention, and Consistent Behaviour across Trials

no code implementations17 May 2019 Saikat Chatterjee, Alireza M. Javid, Mostafa Sadeghi, Shumpei Kikuta, Dong Liu, Partha P. Mitra, Mikael Skoglund

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.

Image Classification

Progressive Learning for Systematic Design of Large Neural Networks

1 code implementation23 Oct 2017 Saikat Chatterjee, Alireza M. Javid, Mostafa Sadeghi, Partha P. Mitra, Mikael Skoglund

The developed network is expected to show good generalization power due to appropriate regularization and use of random weights in the layers.

A Study on Clustering for Clustering Based Image De-Noising

no code implementations6 Jan 2015 Hossein Bakhshi Golestani, Mohsen Joneidi, Mostafa Sadeghi

In the present paper, we suggest a method based on global clustering of image constructing blocks.

Dictionary Learning

Optimization of Clustering for Clustering-based Image Denoising

no code implementations12 Jun 2013 Mohsen Joneidi, Mostafa Sadeghi

In this paper, the problem of de-noising of an image contaminated with additive white Gaussian noise (AWGN) is studied.

Dictionary Learning Image Denoising

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