Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: FurcaNeXt: End-to-end monaural speech separation with dynamic gated
  dilated temporal convolutional networks   Deep dilated temporal convolutional networks (TCN) have been proved to be
very effective in sequence modeling. In this paper we propose several
improvements of TCN for end-to-end approach to monaural speech separation,
which consists of 1) multi-scale dynamic weighted gated dilated convolutional
pyramids network (FurcaPy), 2) gated TCN with intra-parallel convolutional
components (FurcaPa), 3) weight-shared multi-scale gated TCN (FurcaSh), 4)
dilated TCN with gated difference-convolutional component (FurcaSu), that all
these networks take the mixed utterance of two speakers and maps it to two
separated utterances, where each utterance contains only one speaker's voice.
For the objective, we propose to train the network by directly optimizing
utterance level signal-to-distortion ratio (SDR) in a permutation invariant
training (PIT) style. Our experiments on the the public WSJ0-2mix data corpus
results in 18.4dB SDR improvement, which shows our proposed networks can leads
to performance improvement on the speaker separation task.

30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
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The categories of this paper are: q-fin
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom