Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
11021181 | Expert Systems with Applications | 2019 | 13 Pages |
Abstract
As intelligent transportation systems are becoming more and more prevalent, the relevance of automatic surveillance systems grows larger. While such systems rely heavily on video signals, other types of signals can be used as well to monitor the security of passengers. The present article proposes an audio-based intelligent system for surveillance in public transportation, investigating the use of some state-of-the-art artificial intelligence methods for the automatic detection of screams and shouts. We present test results produced on a database of sounds occurring in subway trains in real working conditions, by classifying sounds into screams, shouts and other categories using different Neural Network architectures. The relevance of these architectures in the analysis of audio signals is analyzed. We report encouraging results, given the difficulty of the task, especially when a high level of surrounding noise is present.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Pierre Laffitte, Yun Wang, David Sodoyer, Laurent Girin,