Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Recurrent Encoder-Decoder Approach with Skip-filtering Connections for
  Monaural Singing Voice Separation   The objective of deep learning methods based on encoder-decoder architectures
for music source separation is to approximate either ideal time-frequency masks
or spectral representations of the target music source(s). The spectral
representations are then used to derive time-frequency masks. In this work we
introduce a method to directly learn time-frequency masks from an observed
mixture magnitude spectrum. We employ recurrent neural networks and train them
using prior knowledge only for the magnitude spectrum of the target source. To
assess the performance of the proposed method, we focus on the task of singing
voice separation. The results from an objective evaluation show that our
proposed method provides comparable results to deep learning based methods
which operate over complicated signal representations. Compared to previous
methods that approximate time-frequency masks, our method has increased
performance of signal to distortion ratio by an average of 3.8 dB.

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The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
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The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
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The categories of this paper are: patt-sol
3
The categories of this paper are: physics
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex