Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Transform: Cocktail Party Source Separation via Probabilistic
  Re-Synthesis   In cocktail party listening scenarios, the human brain is able to separate
competing speech signals. However, the signal processing implemented by the
brain to perform cocktail party listening is not well understood. Here, we
trained two separate convolutive autoencoder deep neural networks (DNN) to
separate monaural and binaural mixtures of two concurrent speech streams. We
then used these DNNs as convolutive deep transform (CDT) devices to perform
probabilistic re-synthesis. The CDTs operated directly in the time-domain. Our
simulations demonstrate that very simple neural networks are capable of
exploiting monaural and binaural information available in a cocktail party
listening scenario.

17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org