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Jesper Lisby Højvang

2 accepted papers

2022

A Bayesian Permutation Training Deep Representation Learning Method for Speech Enhancement with Variational Autoencoder

Yang Xiang, Jesper Lisby Højvang, Morten Højfeldt Rasmussen, Mads Græsbøll Christensen
ICASSP 2022accepted

Recently, variational autoencoder (VAE), a deep representation learning (DRL) model, has been used to perform speech enhancement (SE). However, to the best of our knowledge, current VAE-based SE methods only apply VAE to model speech signal, while noise is modeled using the traditional non-negative…

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2021

A Novel NMF-HMM Speech Enhancement Algorithm Based on Poisson Mixture Model

Yang Xiang, Liming Shi, Jesper Lisby Højvang, Morten Højfeldt Rasmussen, Mads Græsbøll Christensen
ICASSP 2021accepted

In this paper, we propose a novel non-negative matrix factorization (NMF) and hidden Markov model (NMF-HMM) based speech enhancement algorithm, which employs a Poisson mixture model (PMM). Compared to the previously proposed NMF-HMM method, the new algorithm, termed PMM-NMF-HMM, uses the Poisson mix…

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