| Title |
Exploiting spectro-temporal locality in deep learning based acoustic event detection
|
|---|---|
| Published in |
EURASIP Journal on Audio, Speech, and 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 106 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 | 102 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 25 | 24% |
| Researcher | 20 | 19% |
| Student > Master | 17 | 16% |
| Student > Bachelor | 9 | 8% |
| Student > Doctoral Student | 7 | 7% |
| Other | 12 | 11% |
| Unknown | 16 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 35 | 33% |
| Computer Science | 33 | 31% |
| Agricultural and Biological Sciences | 5 | 5% |
| Physics and Astronomy | 5 | 5% |
| Neuroscience | 2 | 2% |
| Other | 5 | 5% |
| Unknown | 21 | 20% |
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
#5,446,629
of 25,373,627 outputs
Outputs from EURASIP Journal on Audio, Speech, and Music Processing
#13
of 131 outputs
Outputs of similar age
#64,551
of 280,720 outputs
Outputs of similar age from EURASIP Journal on Audio, Speech, and Music Processing
#1
of 3 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 131 research outputs from this source. They receive a mean Attention Score of 3.0. This one has done well, scoring higher than 85% 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 280,720 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% 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