Breathing-Based Authentication on Resource-Constrained IoT Devices using Recurrent Neural Networks
Research output: Contribution to journal › Article › Scientific › peer-review
- University of New South Wales
- Singapore Management University
Recurrent neural networks (RNNs) have shown promising results in audio and speech-processing applications. The increasing popularity of Internet of Things (IoT) devices makes a strong case for implementing RNN-based inferences for applications such as acoustics-based authentication and voice commands for smart homes. However, the feasibility and performance of these inferences on resource-constrained devices remain largely unexplored. The authors compare traditional machine-learning models with deep-learning RNN models for an end-to-end authentication system based on breathing acoustics.
|Number of pages||8|
|Publication status||Published - May 2018|
|MoE publication type||A1 Journal article-refereed|