Semi-supervised Multichannel Speech Enhancement with Variational Autoencoders and Non-negative Matrix Factorization

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 t...

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Vydáno v:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 101 - 105
Hlavní autoři: Leglaive, Simon, Girin, Laurent, Horaud, Radu
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.05.2019
Témata:
Adaptation models
Autoencoders
Computational modeling
Inference algorithms
local Gaussian modeling
Monte Carlo expectation-maximization
Monte Carlo methods
Multichannel speech enhancement
Noise
Noise measurement
non-negative matrix factorization
Recording
Signal processing algorithms
Speech enhancement
variational autoencoders
ISSN:2379-190X
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Popis
Shrnutí: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.
ISSN:2379-190X
DOI:10.1109/ICASSP.2019.8683704