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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 freedoi:10.1109/mlsp.2018.8516711 dblp:conf/mlsp/LeglaiveGH18 fatcat:f7k5g4iuxjbdncjrztd2fb7rbe