344 citations · 1.2k across the 37 of their papers we have counts for
39 papers
Mixture of Dynamical Variational Autoencoders for Multi-Source Trajectory Modeling and Separation
Xiaoyu Lin, Laurent Girin, Xavier Alameda-Pineda
In this paper, we propose a latent-variable generative model called mixture of dynamical variational autoencoders (MixDVAE) to model the dynamics of a system composed of multiple m…
Unsupervised speech enhancement with deep dynamical generative speech and noise models
Xiaoyu Lin, Simon Leglaive, Laurent Girin +1
This work builds on a previous work on unsupervised speech enhancement using a dynamical variational autoencoder (DVAE) as the clean speech model and non-negative matrix factorizat…
A multimodal dynamical variational autoencoder for audiovisual speech representation learning
Samir Sadok, Simon Leglaive, Laurent Girin +2
In this paper, we present a multimodal and dynamical VAE (MDVAE) applied to unsupervised audio-visual speech representation learning. The latent space is structured to dissociate t…
Speech Modeling with a Hierarchical Transformer Dynamical VAE
Xiaoyu Lin, Xiaoyu Bie, Simon Leglaive +2
The dynamical variational autoencoders (DVAEs) are a family of latent-variable deep generative models that extends the VAE to model a sequence of observed data and a corresponding…
BERT, can HE predict contrastive focus? Predicting and controlling prominence in neural TTS using a language model
Brooke Stephenson, Laurent Besacier, Laurent Girin +1
Several recent studies have tested the use of transformer language model representations to infer prosodic features for text-to-speech synthesis (TTS). While these studies have exp…
Repeat after me: Self-supervised learning of acoustic-to-articulatory mapping by vocal imitation
Marc-Antoine Georges, Julien Diard, Laurent Girin +2
We propose a computational model of speech production combining a pre-trained neural articulatory synthesizer able to reproduce complex speech stimuli from a limited set of interpr…
HiT-DVAE: Human Motion Generation via Hierarchical Transformer Dynamical VAE
Xiaoyu Bie, Wen Guo, Simon Leglaive +3
Studies on the automatic processing of 3D human pose data have flourished in the recent past. In this paper, we are interested in the generation of plausible and diverse future hum…
Learning and controlling the source-filter representation of speech with a variational autoencoder
Samir Sadok, Simon Leglaive, Laurent Girin +2
Understanding and controlling latent representations in deep generative models is a challenging yet important problem for analyzing, transforming and generating various types of da…
Unsupervised Multiple-Object Tracking with a Dynamical Variational Autoencoder
Xiaoyu Lin, Laurent Girin, Xavier Alameda-Pineda
In this paper, we present an unsupervised probabilistic model and associated estimation algorithm for multi-object tracking (MOT) based on a dynamical variational autoencoder (DVAE…
A Survey of Sound Source Localization with Deep Learning Methods
Pierre-Amaury Grumiaux, Srđan Kitić, Laurent Girin +1
This article is a survey on deep learning methods for single and multiple sound source localization. We are particularly interested in sound source localization in indoor/domestic…
SALADnet: Self-Attentive multisource Localization in the Ambisonics Domain
Pierre-Amaury Grumiaux, Srdan Kitic, Prerak Srivastava +2
In this work, we propose a novel self-attention based neural network for robust multi-speaker localization from Ambisonics recordings. Starting from a state-of-the-art convolutiona…
A Benchmark of Dynamical Variational Autoencoders applied to Speech Spectrogram Modeling
Xiaoyu Bie, Laurent Girin, Simon Leglaive +2
The Variational Autoencoder (VAE) is a powerful deep generative model that is now extensively used to represent high-dimensional complex data via a low-dimensional latent space lea…
Unsupervised Speech Enhancement using Dynamical Variational Auto-Encoders
Xiaoyu Bie, Simon Leglaive, Xavier Alameda-Pineda +1
Dynamical variational autoencoders (DVAEs) are a class of deep generative models with latent variables, dedicated to model time series of high-dimensional data. DVAEs can be consid…
Improved feature extraction for CRNN-based multiple sound source localization
Pierre-Amaury Grumiaux, Srdan Kitic, Laurent Girin +1
In this work, we propose to extend a state-of-the-art multi-source localization system based on a convolutional recurrent neural network and Ambisonics signals. We significantly im…
Learning robust speech representation with an articulatory-regularized variational autoencoder
Marc-Antoine Georges, Laurent Girin, Jean-Luc Schwartz +1
It is increasingly considered that human speech perception and production both rely on articulatory representations. In this paper, we investigate whether this type of representati…
Alternate Endings: Improving Prosody for Incremental Neural TTS with Predicted Future Text Input
Brooke Stephenson, Thomas Hueber, Laurent Girin +1
The prosody of a spoken word is determined by its surrounding context. In incremental text-to-speech synthesis, where the synthesizer produces an output before it has access to the…
Multichannel CRNN for Speaker Counting: an Analysis of Performance
Pierre-Amaury Grumiaux, Srdan Kitic, Laurent Girin +1
Speaker counting is the task of estimating the number of people that are simultaneously speaking in an audio recording. For several audio processing tasks such as speaker diarizati…
Reverberant Sound Localization with a Robot Head Based on Direct-Path Relative Transfer Function
Xiaofei Li, Laurent Girin, Fabien Badeig +1
This paper addresses the problem of sound-source localization (SSL) with a robot head, which remains a challenge in real-world environments. In particular we are interested in loca…
What the Future Brings: Investigating the Impact of Lookahead for Incremental Neural TTS
Brooke Stephenson, Laurent Besacier, Laurent Girin +1
In incremental text to speech synthesis (iTTS), the synthesizer produces an audio output before it has access to the entire input sentence. In this paper, we study the behavior of…
Dynamical Variational Autoencoders: A Comprehensive Review
Laurent Girin, Simon Leglaive, Xiaoyu Bie +3
Variational autoencoders (VAEs) are powerful deep generative models widely used to represent high-dimensional complex data through a low-dimensional latent space learned in an unsu…
High-Resolution Speaker Counting In Reverberant Rooms Using CRNN With Ambisonics Features
Pierre-Amaury Grumiaux, Srdjan Kitic, Laurent Girin +1
Speaker counting is the task of estimating the number of people that are simultaneously speaking in an audio recording. For several audio processing tasks such as speaker diarizati…
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…
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…