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cs.SD2022
Fast and efficient speech enhancement with variational autoencoders
Mostafa Sadeghi, Romain Serizel
Unsupervised speech enhancement based on variational autoencoders has shown promising performance compared with the commonly used supervised methods. This approach involves the use…
cs.CV2022
Audio-visual speech enhancement with a deep Kalman filter generative model
Ali Golmakani, Mostafa Sadeghi, Romain Serizel
Deep latent variable generative models based on variational autoencoder (VAE) have shown promising performance for audiovisual speech enhancement (AVSE). The underlying idea is to…
cs.SD2022
A weighted-variance variational autoencoder model for speech enhancement
Ali Golmakani, Mostafa Sadeghi, Xavier Alameda-Pineda +1
We address speech enhancement based on variational autoencoders, which involves learning a speech prior distribution in the time-frequency (TF) domain. A zero-mean complex-valued G…