Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Attention Fusion Feature for Speech Separation with End-to-End
  Post-filter Method   In this paper, we propose an end-to-end post-filter method with deep
attention fusion features for monaural speaker-independent speech separation.
At first, a time-frequency domain speech separation method is applied as the
pre-separation stage. The aim of pre-separation stage is to separate the
mixture preliminarily. Although this stage can separate the mixture, it still
contains the residual interference. In order to enhance the pre-separated
speech and improve the separation performance further, the end-to-end
post-filter (E2EPF) with deep attention fusion features is proposed. The E2EPF
can make full use of the prior knowledge of the pre-separated speech, which
contributes to speech separation. It is a fully convolutional speech separation
network and uses the waveform as the input features. Firstly, the 1-D
convolutional layer is utilized to extract the deep representation features for
the mixture and pre-separated signals in the time domain. Secondly, to pay more
attention to the outputs of the pre-separation stage, an attention module is
applied to acquire deep attention fusion features, which are extracted by
computing the similarity between the mixture and the pre-separated speech.
These deep attention fusion features are conducive to reduce the interference
and enhance the pre-separated speech. Finally, these features are sent to the
post-filter to estimate each target signals. Experimental results on the
WSJ0-2mix dataset show that the proposed method outperforms the
state-of-the-art speech separation method. Compared with the pre-separation
method, our proposed method can acquire 64.1%, 60.2%, 25.6% and 7.5% relative
improvements in scale-invariant source-to-noise ratio (SI-SNR), the
signal-to-distortion ratio (SDR), the perceptual evaluation of speech quality
(PESQ) and the short-time objective intelligibility (STOI) measures,
respectively.

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The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
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The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
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The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: econ
7
The categories of this paper are: hep-th
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The categories of this paper are: funct-an
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The categories of this paper are: q-alg
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The categories of this paper are: atom-ph
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The categories of this paper are: stat
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The categories of this paper are: gr-qc