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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Adaptive Multi-Rate Compression Effects on Vowel Analysis
|
|---|---|
| Published in |
Frontiers in Bioengineering and Biotechnology, August 2015
|
| DOI | 10.3389/fbioe.2015.00118 |
| Pubmed ID | |
| Authors |
David Ireland, Christina Knuepffer, Simon J. McBride |
| Abstract |
Signal processing on digitally sampled vowel sounds for the detection of pathological voices has been firmly established. This work examines compression artifacts on vowel speech samples that have been compressed using the adaptive multi-rate codec at various bit-rates. Whereas previous work has used the sensitivity of machine learning algorithm to test for accuracy, this work examines the changes in the extracted speech features themselves and thus report new findings on the usefulness of a particular feature. We believe this work will have potential impact for future research on remote monitoring as the identification and exclusion of an ill-defined speech feature that has been hitherto used, will ultimately increase the robustness of the system. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 33% |
| Switzerland | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 11 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 2 | 18% |
| Lecturer | 1 | 9% |
| Professor | 1 | 9% |
| Student > Ph. D. Student | 1 | 9% |
| Researcher | 1 | 9% |
| Other | 1 | 9% |
| Unknown | 4 | 36% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 2 | 18% |
| Engineering | 2 | 18% |
| Neuroscience | 1 | 9% |
| Psychology | 1 | 9% |
| Unknown | 5 | 45% |
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 25 August 2015.
All research outputs
#14,599,159
of 25,371,288 outputs
Outputs from Frontiers in Bioengineering and Biotechnology
#1,724
of 8,500 outputs
Outputs of similar age
#127,956
of 277,474 outputs
Outputs of similar age from Frontiers in Bioengineering and Biotechnology
#8
of 49 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,500 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done well, scoring higher than 79% 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 277,474 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 53% of its contemporaries.
We're also able to compare this research output to 49 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.