Name
Title | ||
|---|---|---|
BreathRNNet: Breathing Based Authentication on Resource-Constrained IoT Devices using RNNs. |
Abstract | ||
|---|---|---|
Recurrent neural networks (RNNs) have shown promising results in audio and speech processing applications due to their strong capabilities in modelling sequential data. In many applications, RNNs tend to outperform conventional models based on GMM/UBMs and i-vectors. Increasing popularity of IoT devices makes a strong case for implementing RNN based inferences for applications such as acoustics based authentication, voice commands, and edge analytics for smart homes. Nonetheless, the feasibility and performance of RNN based inferences on resources-constrained IoT devices remain largely unexplored. In this paper, we investigate the feasibility of using RNNs for an end-to-end authentication system based on breathing acoustics. We evaluate the performance of RNN models on three types of devices; smartphone, smartwatch, and Raspberry Pi and show that unlike CNN models, RNN models can be easily ported onto resource-constrained devices without a significant loss in accuracy. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Cryptography and Security | Speech processing,Authentication,Computer science,Computer security,Internet of Things,Recurrent neural network,Porting,Analytics,Smartwatch,Voice command device |
DocType | Volume | Citations |
Journal | abs/1709.07626 | 0 |
PageRank | References | Authors |
0.34 | 5 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jagmohan Chauhan | 1 | 33 | 4.93 |
| Suranga Seneviratne | 2 | 155 | 18.43 |
| Yining Hu | 3 | 0 | 1.35 |
| Archan Misra | 4 | 1688 | 149.25 |
| Aruna Seneviratne | 5 | 1086 | 121.47 |
| Youngki Lee | 6 | 832 | 70.33 |