Search Results for author:
Found 8 papers, 3 papers with code
Using Random Codebooks for Audio Neural AutoEncoders
Latent representation learning has been an active field of study for decades in numerous applications.
Learning Source Disentanglement in Neural Audio Codec
Neural audio codecs have significantly advanced audio compression by efficiently converting continuous audio signals into discrete tokens.
WaveTransfer: A Flexible End-to-end Multi-instrument Timbre Transfer with Diffusion
As diffusion-based deep generative models gain prevalence, researchers are actively investigating their potential applications across various domains, including music synthesis and style alteration.
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.
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.
Multi-Person Extreme Motion Prediction
In this paper, we explore this problem when dealing with humans performing collaborative tasks, we seek to predict the future motion of two interacted persons given two sequences of their past skeletons.
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.
