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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A Comparison of Low-Complexity Real-Time Feature Extraction for Neuromorphic Speech Recognition
|
|---|---|
| Published in |
Frontiers in Neuroscience, March 2018
|
| DOI | 10.3389/fnins.2018.00160 |
| Pubmed ID | |
| Authors |
Jyotibdha Acharya, Aakash Patil, Xiaoya Li, Yi Chen, Shih-Chii Liu, Arindam Basu |
| Abstract |
This paper presents a real-time, low-complexity neuromorphic speech recognition system using a spiking silicon cochlea, a feature extraction module and a population encoding method based Neural Engineering Framework (NEF)/Extreme Learning Machine (ELM) classifier IC. Several feature extraction methods with varying memory and computational complexity are presented along with their corresponding classification accuracies. On the N-TIDIGITS18 dataset, we show that a fixed bin size based feature extraction method that votes across both time and spike count features can achieve an accuracy of 95% in software similar to previously report methods that use fixed number of bins per sample while using ~3× less energy and ~25× less memory for feature extraction (~1.5× less overall). Hardware measurements for the same topology show a slightly reduced accuracy of 94% that can be attributed to the extra correlations in hardware random weights. The hardware accuracy can be increased by further increasing the number of hidden nodes in ELM at the cost of memory and energy. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 25 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 20% |
| Student > Master | 4 | 16% |
| Other | 2 | 8% |
| Professor > Associate Professor | 2 | 8% |
| Researcher | 2 | 8% |
| Other | 3 | 12% |
| Unknown | 7 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 7 | 28% |
| Computer Science | 3 | 12% |
| Physics and Astronomy | 2 | 8% |
| Mathematics | 1 | 4% |
| Neuroscience | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 10 | 40% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 03 April 2018.
All research outputs
#19,951,180
of 25,382,440 outputs
Outputs from Frontiers in Neuroscience
#8,672
of 11,542 outputs
Outputs of similar age
#252,967
of 344,304 outputs
Outputs of similar age from Frontiers in Neuroscience
#207
of 249 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,542 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
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We're also able to compare this research output to 249 others from the same source and published within six weeks on either side of this one. This one is in the 7th percentile – i.e., 7% of its contemporaries scored the same or lower than it.