Speech enhancement with variational autoencoders and alpha-stable distributions

Simon Leglaive 1 Umut Simsekli 2 Antoine Liutkus 3 Laurent Girin 4 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
3 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
4 GIPSA-CRISSP - CRISSP
GIPSA-DPC - Département Parole et Cognition, GIPSA-PPC - GIPSA Pôle Parole et Cognition
Abstract : his paper focuses on single-channel semi-supervised speech en-hancement. We learn a speaker-independent deep generative speechmodel using the framework of variational autoencoders. The noisemodel remains unsupervised because we do not assume prior knowl-edge of the noisy recording environment. In this context, our con-tribution is to propose a noise model based on alpha-stable distribu-tions, instead of the more conventional Gaussian non-negative ma-trix factorization approach found in previous studies. We develop aMonte Carlo expectation-maximization algorithm for estimating themodel parameters at test time. Experimental results show the supe-riority of the proposed approach both in terms of perceptual qualityand intelligibility of the enhanced speech signal.
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Simon Leglaive, Umut Simsekli, Antoine Liutkus, Laurent Girin, Radu Horaud. Speech enhancement with variational autoencoders and alpha-stable distributions. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing, May 2019, Brighton, United Kingdom. pp.541-545, ⟨10.1109/ICASSP.2019.8682546⟩. ⟨hal-02005106⟩

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