Search Results for author:
Found 3 papers, 0 papers with code
A Two-Stage Deep Representation Learning-Based Speech Enhancement Method Using Variational Autoencoder and Adversarial Training
To obtain a higher quality enhanced speech, we propose a two-stage DRL-based SE method through adversarial training.
A deep representation learning speech enhancement method using $β$-VAE
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 the traditional deep neural network-based (DNN) method could be improved by deep representation learning (DRL).
A Bayesian Permutation training deep representation learning method for speech enhancement with variational autoencoder
This means that the proposed method can apply the VAE to model both speech and noise signals, which is totally different from the previous VAE-based SE works.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
