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

Simon Leglaive 1 Laurent Girin 2 Radu Horaud 1
1 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
2 GIPSA-CRISSP - CRISSP
GIPSA-DPC - Département Parole et Cognition
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.
Type de document :
Communication dans un congrès
IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), May 2019, Brighton, United Kingdom. IEEE, pp.1-5
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https://hal.inria.fr/hal-02005102
Contributeur : Simon Leglaive <>
Soumis le : vendredi 8 février 2019 - 11:20:54
Dernière modification le : vendredi 8 février 2019 - 13:48:49

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  • HAL Id : hal-02005102, version 1

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Simon Leglaive, Laurent Girin, Radu Horaud. Semi-supervised multichannel speech enhancement with variational autoencoders and non-negative matrix factorization. IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), May 2019, Brighton, United Kingdom. IEEE, pp.1-5. 〈hal-02005102〉

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