1 citations · 3 across the 4 of their papers we have counts for
4 papers
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…
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…
A Speech Enhancement Algorithm based on Non-negative Hidden Markov Model and Kullback-Leibler Divergence
Yang Xiang, Liming Shi, Jesper Lisby Højvang +2
In this paper, we propose a novel supervised single-channel speech enhancement method combing the the Kullback-Leibler divergence-based non-negative matrix factorization (NMF) and…