Name
Abstract | ||
|---|---|---|
In the context of the Internet of Things, sound sensing applications are required to run on embedded platforms where notions of product pricing and form factor impose hard constraints on the available computing power. Whereas Automatic Environmental Sound Recognition AESR algorithms are most often developed with limited consideration for computational cost, this paper seeks which AESR algorithm can make the most of a limited amount of computing power by comparing the sound classification performance as a function of its computational cost. Results suggest that Deep Neural Networks yield the best ratio of sound classification accuracy across a range of computational costs, while Gaussian Mixture Models offer a reasonable accuracy at a consistently small cost, and Support Vector Machines stand between both in terms of compromise between accuracy and computational cost. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TASLP.2016.2592698 | IEEE/ACM Trans. Audio, Speech & Language Processing |
Keywords | DocType | Volume |
Computational efficiency,Speech,Speech recognition,Acoustics,Internet of things,IEEE transactions,Speech processing | Journal | abs/1607.04589 |
Issue | ISSN | Citations |
11 | 2329-9290 | 12 |
PageRank | References | Authors |
0.87 | 33 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siddharth Sigtia | 1 | 127 | 8.56 |
| Adam M. Stark | 2 | 27 | 3.72 |
| Sacha Krstulovic | 3 | 106 | 11.97 |
| M. D. Plumbley | 4 | 1915 | 202.38 |