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Exploiting spectro-temporal locality in deep learning based acoustic event detection

Overview of attention for article published in EURASIP Journal on Audio Speech, & Music Processing, September 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (72nd percentile)

Mentioned by

patent
2 patents

Citations

dimensions_citation
36 Dimensions

Readers on

mendeley
82 Mendeley
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Title
Exploiting spectro-temporal locality in deep learning based acoustic event detection
Published in
EURASIP Journal on Audio Speech, & Music Processing, September 2015
DOI 10.1186/s13636-015-0069-2
Authors

Miquel Espi, Masakiyo Fujimoto, Keisuke Kinoshita, Tomohiro Nakatani

Mendeley readers

The data shown below were compiled from readership statistics for 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Turkey 1 1%
Netherlands 1 1%
Poland 1 1%
France 1 1%
Unknown 78 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 23%
Researcher 18 22%
Student > Master 16 20%
Student > Doctoral Student 6 7%
Student > Bachelor 6 7%
Other 10 12%
Unknown 7 9%
Readers by discipline Count As %
Computer Science 30 37%
Engineering 28 34%
Physics and Astronomy 5 6%
Neuroscience 2 2%
Environmental Science 2 2%
Other 6 7%
Unknown 9 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 October 2019.
All research outputs
#2,853,700
of 14,913,531 outputs
Outputs from EURASIP Journal on Audio Speech, & Music Processing
#5
of 84 outputs
Outputs of similar age
#75,571
of 271,223 outputs
Outputs of similar age from EURASIP Journal on Audio Speech, & Music Processing
#1
of 1 outputs
Altmetric has tracked 14,913,531 research outputs across all sources so far. Compared to these this one has done well and is in the 77th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 84 research outputs from this source. They receive a mean Attention Score of 2.0. This one has done particularly well, scoring higher than 92% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 271,223 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them