2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

Paper Detail

Paper IDAUD-24.5
Paper Title VARIATIONAL AUTOENCODER FOR SPEECH ENHANCEMENT WITH A NOISE-AWARE ENCODER
Authors Huajian Fang, Guillaume Carbajal, Stefan Wermter, Timo Gerkmann, Universität Hamburg, Germany
SessionAUD-24: Signal Enhancement and Restoration 1: Deep Learning
LocationGather.Town
Session Time:Thursday, 10 June, 16:30 - 17:15
Presentation Time:Thursday, 10 June, 16:30 - 17:15
Presentation Poster
Topic Audio and Acoustic Signal Processing: [AUD-SEN] Signal Enhancement and Restoration
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Virtual Presentation  Click here to watch in the Virtual Conference
Abstract Recently, a generative variational autoencoder (VAE) has been proposed for speech enhancement to model speech statistics. However, this approach only uses clean speech in the training phase, making the estimation particularly sensitive to noise presence, especially in low signal-to-noise ratios (SNRs). To increase the robustness of the VAE, we propose to include noise information in the training phase by using a noise-aware encoder trained on noisy-clean speech pairs. We evaluate our approach on real recordings of different noisy environments and acoustic conditions using two different noise datasets. We show that our proposed noise-aware VAE outperforms the standard VAE in terms of overall distortion without increasing the number of model parameters. At the same time, we demonstrate that our model is capable of generalizing to unseen noise conditions better than a supervised feedforward deep neural network (DNN). Furthermore, we demonstrate the robustness of the model performance to a reduction of the noisy-clean speech training data size.