Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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.

34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas