Smart City Gnosys
Smart city article details
| Title | Multi-Task Deep Learning Approach For Sound Event Recognition And Tracking |
|---|---|
| ID_Doc | 38435 |
| Authors | Chen T.-S.; Chen M.-J.; Chen T.-C. |
| Year | 2024 |
| Published | International Journal of Ad Hoc and Ubiquitous Computing, 46, 2 |
| DOI | http://dx.doi.org/10.1504/IJAHUC.2024.138747 |
| Abstract | In smart cities, it is important to detect abnormal activities through cameras. However, cameras have limitations such as blind spots and blocked areas that can result in detection failures. Sound, on the other hand, is less likely to be obstructed. This paper proposes using microphone arrays to identify sound events, predict their locations, and track their trajectories using multi-task deep learning approaches. Experimental results show high predictive accuracy. Finally, the proposed models are also converted to quantised versions and deployed on embedded devices in vehicles to analyse memory footprint and execution time. Copyright © 2024 Inderscience Enterprises Ltd. |
| Author Keywords | deep learning; localisation; microphone arrays; sound event classification; sound tracking |
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