10 citations · 16 across the 11 of their papers we have counts for
4 papers · 1 filter
A Two-Stage Deep Representation Learning-Based Speech Enhancement Method Using Variational Autoencoder and Adversarial Training
Yang Xiang, Jesper Lisby Højvang, Morten Højfeldt Rasmussen +1
This paper focuses on leveraging deep representation learning (DRL) for speech enhancement (SE). In general, the performance of the deep neural network (DNN) is heavily dependent o…
Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model using Subspace Perturbation
Qiongxiu Li, Jaron Skovsted Gundersen, Katrine Tjell +2
Privacy has become a major concern in machine learning. In fact, the federated learning is motivated by the privacy concern as it does not allow to transmit the private data but on…
A deep representation learning speech enhancement method using -VAE
Yang Xiang, Jesper Lisby Højvang, Morten Højfeldt Rasmussen +1
In previous work, we proposed a variational autoencoder-based (VAE) Bayesian permutation training speech enhancement (SE) method (PVAE) which indicated that the SE performance of t…
A Bayesian Permutation training deep representation learning method for speech enhancement with variational autoencoder
Yang Xiang, Jesper Lisby Højvang, Morten Højfeldt Rasmussen +1
Recently, variational autoencoder (VAE), a deep representation learning (DRL) model, has been used to perform speech enhancement (SE). However, to the best of our knowledge, curren…