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Speech enhancement with variational autoencoders and alpha-stable distributions

Simon Leglaive 1 Umut Şimşekli 2, 3 Antoine Liutkus 4 Laurent Girin 5 Radu Horaud 1 
1 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology, LJK - Laboratoire Jean Kuntzmann
2 S2A - Signal, Statistique et Apprentissage
LTCI - Laboratoire Traitement et Communication de l'Information
4 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
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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Submitted on : Friday, February 8, 2019 - 11:25:30 AM
Last modification on : Friday, August 5, 2022 - 3:03:28 PM
Long-term archiving on: : Thursday, May 9, 2019 - 1:46:30 PM


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Simon Leglaive, Umut Şimşekli, Antoine Liutkus, Laurent Girin, Radu Horaud. Speech enhancement with variational autoencoders and alpha-stable distributions. ICASSP 2019 - 44th 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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