Name
Title | ||
|---|---|---|
Convolutional Neural Networks for audio classification on ultra low power IoT devices |
Abstract | ||
|---|---|---|
Sound classification usually requires heavy resources in terms of computation, memory, and energy to achieve good accuracy. However, it is possible to enable a more efficient and accurate audio recognition on pervasive IoT platforms through specific optimizations. In this paper, a solution based on convolutional neural networks is proposed for audio classification on resource-constrained wireless ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/BlackSeaCom52164.2021.9527865 | 2021 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom) |
Keywords | DocType | ISBN |
Wireless communication,Conferences,Neural networks,Memory management,Convolutional neural networks,Low-power electronics,Optimization | Conference | 978-1-6654-0308-5 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alessandro Andreadis | 1 | 37 | 10.36 |
| Giovanni Giambene | 2 | 383 | 47.79 |
| Riccardo Zambon | 3 | 18 | 5.37 |