Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SkiM: Skipping Memory LSTM for Low-Latency Real-Time Continuous Speech
  Separation   Continuous speech separation for meeting pre-processing has recently become a
focused research topic. Compared to the data in utterance-level speech
separation, the meeting-style audio stream lasts longer, has an uncertain
number of speakers. We adopt the time-domain speech separation method and the
recently proposed Graph-PIT to build a super low-latency online speech
separation model, which is very important for the real application. The
low-latency time-domain encoder with a small stride leads to an extremely long
feature sequence. We proposed a simple yet efficient model named Skipping
Memory (SkiM) for the long sequence modeling. Experimental results show that
SkiM achieves on par or even better separation performance than DPRNN.
Meanwhile, the computational cost of SkiM is reduced by 75% compared to DPRNN.
The strong long sequence modeling capability and low computational cost make
SkiM a suitable model for online CSS applications. Our fastest real-time model
gets 17.1 dB signal-to-distortion (SDR) improvement with less than
1-millisecond latency in the simulated meeting-style evaluation.

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The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
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The categories of this paper are: dg-ga
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The categories of this paper are: stat
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The categories of this paper are: math
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
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The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
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The categories of this paper are: funct-an
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The categories of this paper are: chem-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-th
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The categories of this paper are: patt-sol