Assessing the Performances of different Neural Network Architectures for the Detection of Screams and Shouts in Public Transportation

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.
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Pierre Laffitte, Yun Wang, David Sodoyer, Laurent Girin. Assessing the Performances of different Neural Network Architectures for the Detection of Screams and Shouts in Public Transportation. Expert Systems With Applications, 2019, 117, pp29-41. ⟨10.1016/j.eswa.2018.08.052⟩. ⟨hal-01892436⟩

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