Unsupervised speech enhancement with deep dynamical generative speech and noise models

• URL: http://arxiv.org/abs/2306.07820v1
• Date: Tue, 13 Jun 2023 14:52:35 GMT
• Title: Unsupervised speech enhancement with deep dynamical generative speech and noise models
• Authors: Xiaoyu Lin, Simon Leglaive, Laurent Girin, Xavier Alameda-Pineda
• Abstract summary: This work builds on a previous work on unsupervised speech enhancement using a dynamical variational autoencoder (DVAE) as the clean speech model and non-negative matrix factorization (NMF) as the noise model. We propose to replace the NMF noise model with a deep dynamical generative model (DDGM) depending either on the DVAE latent variables, or on the noisy observations, or on both.
• Score: 26.051535142743166
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This work builds on a previous work on unsupervised speech enhancement using a dynamical variational autoencoder (DVAE) as the clean speech model and non-negative matrix factorization (NMF) as the noise model. We propose to replace the NMF noise model with a deep dynamical generative model (DDGM) depending either on the DVAE latent variables, or on the noisy observations, or on both. This DDGM can be trained in three configurations: noise-agnostic, noise-dependent and noise adaptation after noise-dependent training. Experimental results show that the proposed method achieves competitive performance compared to state-of-the-art unsupervised speech enhancement methods, while the noise-dependent training configuration yields a much more time-efficient inference process.

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