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arXiv · 1811.06713

Semi-supervised multichannel speech enhancement with variational autoencoders and non-negative matrix factorization

Simon Leglaive·Laurent Girin·Radu Horaud
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Abstract

In this paper we address speaker-independent multichannel speech enhancement in unknown noisy environments. Our work is based on a well-established multichannel local Gaussian modeling framework. We propose to use a neural network for modeling the speech spectro-temporal content. The parameters of this supervised model are learned using the framework of variational autoencoders. The noisy recording environment is supposed to be unknown, so the noise spectro-temporal modeling remains unsupervised and is based on non-negative matrix factorization (NMF). We develop a Monte Carlo expectation-maximization algorithm and we experimentally show that the proposed approach outperforms its NMF-based counterpart, where speech is modeled using supervised NMF.

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Simon Leglaive, Laurent Girin, Radu Horaud. 2019-04-30. Semi-supervised multichannel speech enhancement with variational autoencoders and non-negative matrix factorization. https://doi.org/10.1109/icassp.2019.8683704

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