Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Guided Variational Autoencoder for Speech Enhancement With a Supervised
  Classifier   Recently, variational autoencoders have been successfully used to learn a
probabilistic prior over speech signals, which is then used to perform speech
enhancement. However, variational autoencoders are trained on clean speech
only, which results in a limited ability of extracting the speech signal from
noisy speech compared to supervised approaches. In this paper, we propose to
guide the variational autoencoder with a supervised classifier separately
trained on noisy speech. The estimated label is a high-level categorical
variable describing the speech signal (e.g. speech activity) allowing for a
more informed latent distribution compared to the standard variational
autoencoder. We evaluate our method with different types of labels on real
recordings of different noisy environments. Provided that the label better
informs the latent distribution and that the classifier achieves good
performance, the proposed approach outperforms the standard variational
autoencoder and a conventional neural network-based supervised approach.

37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph