7 research outputs found
A BAYESIAN PERMUTATION TRAINING DEEP REPRESENTATION LEARNING METHOD FOR SPEECH ENHANCEMENT WITH VARIATIONAL AUTOENCODER
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 the model
speech signal, while noise is modeled using the traditional non-negative matrix
factorization (NMF) model. One of the most important reasons for using NMF is
that these VAE-based methods cannot disentangle the speech and noise latent
variables from the observed signal. Based on Bayesian theory, this paper
derives a novel variational lower bound for VAE, which ensures that VAE can be
trained in supervision, and can disentangle speech and noise latent variables
from the observed signal. This means that the proposed method can apply the VAE
to model both speech and noise signals, which is totally different from the
previous VAE-based SE works. More specifically, the proposed DRL method can
learn to impose speech and noise signal priors to different sets of latent
variables for SE. The experimental results show that the proposed method can
not only disentangle speech and noise latent variables from the observed signal
but also obtain a higher scale-invariant signal-to-distortion ratio and speech
quality score than the similar deep neural network-based (DNN) SE method.Comment: Accepted by ICASSP 202
A deep representation learning speech enhancement method using -VAE
In previous work, we proposed a variational autoencoder-based (VAE) Bayesian
permutation training speech enhancement (SE) method (PVAE) which indicated that
the SE performance of the traditional deep neural network-based (DNN) method
could be improved by deep representation learning (DRL). Based on our previous
work, we in this paper propose to use -VAE to further improve PVAE's
ability of representation learning. More specifically, our -VAE can
improve PVAE's capacity of disentangling different latent variables from the
observed signal without the trade-off problem between disentanglement and
signal reconstruction. This trade-off problem widely exists in previous
-VAE algorithms. Unlike the previous -VAE algorithms, the
proposed -VAE strategy can also be used to optimize the DNN's structure.
This means that the proposed method can not only improve PVAE's SE performance
but also reduce the number of PVAE training parameters. The experimental
results show that the proposed method can acquire better speech and noise
latent representation than PVAE. Meanwhile, it also obtains a higher
scale-invariant signal-to-distortion ratio, speech quality, and speech
intelligibility.Comment: Submitted to Eurosipc