Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Rule-embedded network for audio-visual voice activity detection in live
  musical video streams   Detecting anchor's voice in live musical streams is an important
preprocessing for music and speech signal processing. Existing approaches to
voice activity detection (VAD) primarily rely on audio, however, audio-based
VAD is difficult to effectively focus on the target voice in noisy
environments. With the help of visual information, this paper proposes a
rule-embedded network to fuse the audio-visual (A-V) inputs to help the model
better detect target voice. The core role of the rule in the model is to
coordinate the relation between the bi-modal information and use visual
representations as the mask to filter out the information of non-target sound.
Experiments show that: 1) with the help of cross-modal fusion by the proposed
rule, the detection result of A-V branch outperforms that of audio branch; 2)
the performance of bi-modal model far outperforms that of audio-only models,
indicating that the incorporation of both audio and visual signals is highly
beneficial for VAD. To attract more attention to the cross-modal music and
audio signal processing, a new live musical video corpus with frame-level label
is introduced.

9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
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The categories of this paper are: adap-org
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The categories of this paper are: acc-phys
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The categories of this paper are: bayes-an
2
The categories of this paper are: math
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The categories of this paper are: mtrl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: dg-ga