Publication Type
Journal Article
Version
publishedVersion
Publication Date
5-2018
Abstract
Recurrent neural networks (RNNs) have shown promising resultsin audio and speech-processing applications. The increasingpopularity of Internet of Things (IoT) devices makes a strongcase for implementing RNN-based inferences for applicationssuch as acoustics-based authentication and voice commandsfor smart homes. However, the feasibility and performance ofthese inferences on resource-constrained devices remain largelyunexplored. The authors compare traditional machine-learningmodels with deep-learning RNN models for an end-to-endauthentication system based on breathing acoustics.
Discipline
Digital Communications and Networking | OS and Networks
Research Areas
Data Science and Engineering
Publication
Computer
Volume
51
Issue
5
First Page
60
Last Page
67
ISSN
0018-9162
Identifier
10.1109/MC.2018.2381119
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
CHAUHAN, Jagmohan; SENEVIRATNE, Suranga; HU, Yining; MISRA, Archan; SENEVIRATNE, Aruna; and LEE, Youngki.
Breathing-based authentication on resource-constrained IoT devices using recurrent neural networks. (2018). Computer. 51, (5), 60-67.
Available at: https://ink.library.smu.edu.sg/sis_research/4054
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/MC.2018.2381119