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)

Additional URL

https://doi.org/10.1109/MC.2018.2381119

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