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author:

Zhe, He (Zhe, He.) [1] | Ying, Li (Ying, Li.) [2] (Scholars:李应)

Indexed by:

EI Scopus

Abstract:

As a technology of context analysis, the detective method of polyphonic sound event detection has a widespread prospect of application. In this paper, a detective method of polyphonic sound event were proposed to resolve the challenge in IEEE DCASE2017 task 3 based on full convolutional DenseNet. Relevant results illustrated that the method is higher in F-score and lower in ER than the baseline method proposed by IEEE DCASE2017 based on DNNs, and also get higher performance-7.4% higher in F-score and 3% lower in ER than the best method in IEEE DCASE2017 challenge based on CRNN. © 2018 IEEE.

Keyword:

Audio recordings Big data Blockchain Cloud computing Convolution

Community:

  • [ 1 ] [Zhe, He]College of Mathematics and Computer Science, Fuzhou University Fuzhou, Fujian; 350116, China
  • [ 2 ] [Zhe, He]Key Lab of Information Security of Network Systems, Fuzhou University, Fuzhou, Fujian; 350116, China
  • [ 3 ] [Ying, Li]College of Mathematics and Computer Science, Fuzhou University Fuzhou, Fujian; 350116, China
  • [ 4 ] [Ying, Li]Key Lab of Information Security of Network Systems, Fuzhou University, Fuzhou, Fujian; 350116, China

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Year: 2018

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 2

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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