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Alpha-stable low-rank plus residual decomposition for speech enhancement

Umut Şimşekli 1 Halil Erdogan 2 Simon Leglaive 1 Antoine Liutkus 3 Roland Badeau 1 Gael Richard 1 
3 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : In this study, we propose a novel probabilistic model for separating clean speech signals from noisy mixtures by decomposing the mixture spectrograms into a structured speech part and a more flexible residual part. The main novelty in our model is that it uses a family of heavy-tailed distributions, so called the α-stable distributions, for modeling the residual signal. We develop an expectation-maximization algorithm for parameter estimation and a Monte Carlo scheme for posterior estimation of the clean speech. Our experiments show that the proposed method outperforms relevant factorization-based algorithms by a significant margin.
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Submitted on : Thursday, February 22, 2018 - 7:51:25 AM
Last modification on : Friday, August 5, 2022 - 3:03:28 PM
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Umut Şimşekli, Halil Erdogan, Simon Leglaive, Antoine Liutkus, Roland Badeau, et al.. Alpha-stable low-rank plus residual decomposition for speech enhancement. ICASSP: International Conference on Acoustics, Speech, and Signal Processing, Apr 2018, Calgary, Canada. pp.651-655, ⟨10.1109/ICASSP.2018.8461539⟩. ⟨hal-01714909⟩



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