Variational Autoencoder for Speech Enhancement with a Noise-Aware Encoder
| Autor: | Huajian Fang, Stefan Wermter, Guillaume Carbajal, Timo Gerkmann |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Sound (cs.SD) Artificial neural network Computer science Speech recognition 010501 environmental sciences 01 natural sciences Autoencoder Computer Science - Sound Machine Learning (cs.LG) Speech enhancement 030507 speech-language pathology & audiology 03 medical and health sciences Noise Robustness (computer science) Nonlinear distortion Audio and Speech Processing (eess.AS) Distortion FOS: Electrical engineering electronic engineering information engineering 0305 other medical science Encoder 0105 earth and related environmental sciences Electrical Engineering and Systems Science - Audio and Speech Processing |
| Zdroj: | ICASSP |
| DOI: | 10.48550/arxiv.2102.08706 |
| Popis: | 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. Comment: ICASSP 2021. (c) 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works |
| Databáze: | OpenAIRE |
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