no code implementations • 16 Nov 2022 • Yang Xiang, Jesper Lisby Højvang, Morten Højfeldt Rasmussen, Mads Græsbøll Christensen
To obtain a higher quality enhanced speech, we propose a two-stage DRL-based SE method through adversarial training.
no code implementations • 11 May 2022 • Yang Xiang, Jesper Lisby Højvang, Morten Højfeldt Rasmussen, Mads Græsbøll Christensen
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).
no code implementations • 24 Jan 2022 • Yang Xiang, Jesper Lisby Højvang, Morten Højfeldt Rasmussen, Mads Græsbøll Christensen
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.