336 citations · 1.4k across the 70 of their papers we have counts for
22 papers · 1 filter
Test-time adaptation for speech enhancement with an autoregressive speech prior
Sofiene Kammoun, Simon Leglaive, Xavier Alameda-Pineda +1
Test-time adaptation (TTA) offers a promising direction for improving speech enhancement models under mismatched acoustic conditions, without requiring access to labeled target dat…
InsideSSL: Understanding Self-Supervised Speech Representations using a Model-Centric Perspective
Samir Sadok, Xavier Alameda-Pineda
Self-supervised learning (SSL) models, such as Wav2Vec2, HuBERT, and WavLM, have become foundational across a wide range of speech and audio tasks. Despite their success, understan…
The Equalizer: Introducing Shape-Gain Decomposition in Neural Audio Codecs
Samir Sadok, Laurent Girin, Xavier Alameda-Pineda
Neural audio codecs (NACs) typically encode the short-term energy (gain) and normalized structure (shape) of speech/audio signals jointly within the same latent space. As a result,…
Residual Tokens Enhance Masked Autoencoders for Speech Modeling
Samir Sadok, Stéphane Lathuilière, Xavier Alameda-Pineda
Recent speech modeling relies on explicit attributes such as pitch, content, and speaker identity, but these alone cannot capture the full richness of natural speech. We introduce…
Diffusion-based Frameworks for Unsupervised Speech Enhancement
Jean-Eudes Ayilo, Mostafa Sadeghi, Romain Serizel +1
This paper addresses unsupervised diffusion-based single-channel speech enhancement (SE). Prior work in this direction combines a score-based diffusion model trained on clean speec…
Modeling strategies for speech enhancement in the latent space of a neural audio codec
Sofiene Kammoun, Xavier Alameda-Pineda, Simon Leglaive
Neural audio codecs (NACs) provide compact latent speech representations in the form of sequences of continuous vectors or discrete tokens. In this work, we investigate how these t…
Posterior Transition Modeling for Unsupervised Diffusion-Based Speech Enhancement
Mostafa Sadeghi, Jean-Eudes Ayilo, Romain Serizel +1
We explore unsupervised speech enhancement using diffusion models as expressive generative priors for clean speech. Existing approaches guide the reverse diffusion process using no…
AnCoGen: Analysis, Control and Generation of Speech with a Masked Autoencoder
Samir Sadok, Simon Leglaive, Laurent Girin +2
This article introduces AnCoGen, a novel method that leverages a masked autoencoder to unify the analysis, control, and generation of speech signals within a single model. AnCoGen…
Diffusion-based Unsupervised Audio-visual Speech Enhancement
Jean-Eudes Ayilo, Mostafa Sadeghi, Romain Serizel +1
This paper proposes a new unsupervised audio-visual speech enhancement (AVSE) approach that combines a diffusion-based audio-visual speech generative model with a non-negative matr…
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…
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…
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…
The impact of removing head movements on audio-visual speech enhancement
Zhiqi Kang, Mostafa Sadeghi, Radu Horaud +3
This paper investigates the impact of head movements on audio-visual speech enhancement (AVSE). Although being a common conversational feature, head movements have been ignored by…
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…
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…
A cascaded multiple-speaker localization and tracking system
Xiaofei Li, Yutong Ban, Laurent Girin +2
This paper presents an online multiple-speaker localization and tracking method, as the INRIA-Perception contribution to the LOCATA Challenge 2018. First, the recursive least-squar…
Tracking Multiple Audio Sources with the von Mises Distribution and Variational EM
Yutong Ban, Xavier Alameda-PIneda, Christine Evers +1
In this paper we address the problem of simultaneously tracking several moving audio sources, namely the problem of estimating source trajectories from a sequence of observed featu…
Online Localization and Tracking of Multiple Moving Speakers in Reverberant Environments
Xiaofei Li, Yutong Ban, Laurent Girin +2
We address the problem of online localization and tracking of multiple moving speakers in reverberant environments. The paper has the following contributions. We use the direct-pat…
A Variational EM Algorithm for the Separation of Time-Varying Convolutive Audio Mixtures
Dionyssos Kounades-Bastian, Laurent Girin, Xavier Alameda-Pineda +2
This paper addresses the problem of separating audio sources from time-varying convolutive mixtures. We propose a probabilistic framework based on the local complex-Gaussian model…
Sound Representation and Classification Benchmark for Domestic Robots
Maxime Janvier, Xavier Alameda-Pineda, Laurent Girin +1
We address the problem of sound representation and classification and present results of a comparative study in the context of a domestic robotic scenario. A dataset of sounds was…
A Geometric Approach to Sound Source Localization from Time-Delay Estimates
Xavier Alameda-Pineda, Radu Horaud
This paper addresses the problem of sound-source localization from time-delay estimates using arbitrarily-shaped non-coplanar microphone arrays. A novel geometric formulation is pr…