Country | : |
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Department | : | Singapore Management University |
Project Title | : | Breathing-based authentication on resource-constrained IoT devices using recurrent neural networks |
Researcher | : | LEE, Youngki , HU, Yining , SENEVIRATNE, Aruna , MISRA, Archan , SENEVIRATNE, Suranga , CHAUHAN, Jagmohan |
Keyword | : | Digital Communications and Networking , OS and Networks |
Publisher | : | Institutional Knowledge at Singapore Management University |
Year End | : | 2018 |
Identifier | : | https://ink.library.smu.edu.sg/sis_research/4054 , https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5057&context=sis_research |
Source | : | Research Collection School Of Computing and Information Systems |
Abstract / Description | : |
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. |