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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

  • Above-average Attention Score compared to outputs of the same age (61st percentile)

Mentioned by

patent
1 patent

Citations

dimensions_citation
31 Dimensions

Readers on

mendeley
76 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 76 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 72 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 25%
Researcher 17 22%
Student > Master 14 18%
Other 5 7%
Student > Doctoral Student 5 7%
Other 9 12%
Unknown 7 9%
Readers by discipline Count As %
Computer Science 30 39%
Engineering 25 33%
Physics and Astronomy 3 4%
Agricultural and Biological Sciences 2 3%
Environmental Science 2 3%
Other 5 7%
Unknown 9 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 16 November 2017.
All research outputs
#3,470,651
of 12,145,106 outputs
Outputs from EURASIP Journal on Audio Speech, & Music Processing
#4
of 74 outputs
Outputs of similar age
#75,306
of 198,815 outputs
Outputs of similar age from EURASIP Journal on Audio Speech, & Music Processing
#1
of 3 outputs
Altmetric has tracked 12,145,106 research outputs across all sources so far. This one is in the 49th percentile – i.e., 49% of other outputs scored the same or lower than it.
So far Altmetric has tracked 74 research outputs from this source. They receive a mean Attention Score of 1.7. This one has gotten more attention than average, scoring higher than 66% 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 198,815 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 61% of its contemporaries.
We're also able to compare this research output to 3 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