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Found 18 papers, 4 papers with code
Objective and subjective evaluation of speech enhancement methods in the UDASE task of the 7th CHiME challenge
In this paper, we present the objective and subjective evaluations of the systems that were submitted to the CHiME-7 UDASE task, and we provide an analysis of the results.
VQ-HPS: Human Pose and Shape Estimation in a Vector-Quantized Latent Space
Instead of predicting body model parameters or 3D vertex coordinates, our focus is on forecasting the proposed discrete latent representation, which can be decoded into a registered human mesh.
Unsupervised speech enhancement with deep dynamical generative speech and noise models
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 factorization (NMF) as the noise model.
Motion-DVAE: Unsupervised learning for fast human motion denoising
We introduce Motion-DVAE, a motion prior to capture the short-term dependencies of human motion.
A multimodal dynamical variational autoencoder for audiovisual speech representation learning
The latent space is structured to dissociate the latent dynamical factors that are shared between the modalities from those that are specific to each modality.
A vector quantized masked autoencoder for audiovisual speech emotion recognition
While fully-supervised models have been shown to be effective for audiovisual speech emotion recognition (SER), the limited availability of labeled data remains a major challenge in the field.
A vector quantized masked autoencoder for speech emotion recognition
The VQ-MAE-S model is based on a masked autoencoder (MAE) that operates in the discrete latent space of a vector-quantized variational autoencoder.
Ranked #1 on
Speech Emotion Recognition
on EmoDB Dataset
LatentForensics: Towards frugal deepfake detection in the StyleGAN latent space
Combined with other recent studies on the interpretation and manipulation of this latent space, we believe that the proposed approach can further help in developing frugal deepfake classification methods based on interpretable high-level properties of face images.
Speech Modeling with a Hierarchical Transformer Dynamical VAE
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 sequence of latent vectors.
Learning and controlling the source-filter representation of speech with a variational autoencoder
Using only a few seconds of labeled speech signals generated with an artificial speech synthesizer, we propose a method to identify the latent subspaces encoding $f_0$ and the first three formant frequencies, we show that these subspaces are orthogonal, and based on this orthogonality, we develop a method to accurately and independently control the source-filter speech factors within the latent subspaces.
HiT-DVAE: Human Motion Generation via Hierarchical Transformer Dynamical VAE
Studies on the automatic processing of 3D human pose data have flourished in the recent past.
Unsupervised Speech Enhancement using Dynamical Variational Auto-Encoders
We propose an unsupervised speech enhancement algorithm that combines a DVAE speech prior pre-trained on clean speech signals with a noise model based on nonnegative matrix factorization, and we derive a variational expectation-maximization (VEM) algorithm to perform speech enhancement.
Dynamical Variational Autoencoders: A Comprehensive Review
Recently, a series of papers have presented different extensions of the VAE to process sequential data, which model not only the latent space but also the temporal dependencies within a sequence of data vectors and corresponding latent vectors, relying on recurrent neural networks or state-space models.
A Recurrent Variational Autoencoder for Speech Enhancement
This paper presents a generative approach to speech enhancement based on a recurrent variational autoencoder (RVAE).
Audio-visual Speech Enhancement Using Conditional Variational Auto-Encoders
Variational auto-encoders (VAEs) are deep generative latent variable models that can be used for learning the distribution of complex data.
Speech enhancement with variational autoencoders and alpha-stable distributions
This paper focuses on single-channel semi-supervised speech enhancement.
A variance modeling framework based on variational autoencoders for speech enhancement
In this paper we address the problem of enhancing speech signals in noisy mixtures using a source separation approach.
Semi-supervised multichannel speech enhancement with variational autoencoders and non-negative matrix factorization
In this paper we address speaker-independent multichannel speech enhancement in unknown noisy environments.
