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Conference Papers Year : 2018

A variance modeling framework based on variational autoencoders for speech enhancement

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Abstract

In this paper we address the problem of enhancing speech signals in noisy mixtures using a source separation approach. We explore the use of neural networks as an alternative to a popular speech variance model based on supervised non-negative matrix factorization (NMF). More precisely, we use a variational autoencoder as a speaker-independent supervised generative speech model, highlighting the conceptual similarities that this approach shares with its NMF-based counterpart. In order to be free of generalization issues regarding the noisy recording environments, we follow the approach of having a supervised model only for the target speech signal, the noise model being based on unsupervised NMF. We develop a Monte Carlo expectation-maximization algorithm for inferring the latent variables in the variational autoencoder and estimating the unsupervised model parameters. Experiments show that the proposed method outperforms a semi-supervised NMF baseline and a state-of-the-art fully supervised deep learning approach.
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Dates and versions

hal-01832826 , version 1 (12-07-2018)

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Simon Leglaive, Laurent Girin, Radu Horaud. A variance modeling framework based on variational autoencoders for speech enhancement. MLSP 2018 - IEEE 28th International Workshop on Machine Learning for Signal Processing, Sep 2018, Aalborg, Denmark. pp.1-6, ⟨10.1109/MLSP.2018.8516711⟩. ⟨hal-01832826⟩
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