Abstract
Violence continues being an important problem in the society. Thousands of people suffer its effects every day and statistics show this number has maintained or almost increased recently. In the modern environment of smart cities there is a necessity to develop a system capable of detecting if a violent situation is taking place or not. In this paper we present an automatic acoustic violence detection system for smart cities, integrating both signal processing and pattern recognition techniques. The proposed software has been implemented in three steps: feature extraction in time and frequency domain, genetic algorithm implementation in order to select the best features, and classification to take a binary decision. Results derived from the experiments show that MFCCs are the best features for violence detection, and others like pitch or short time energy have also a good performance. In other words, features that can distinguish between voiced and unvoiced frames seem to be a good election for violence detection in real environments.
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Acknowledgements
This work has been funded by the Spanish Ministry of Economy and Competitiveness (under project TEC2015-67387-C4-4-R, funds Spain/FEDER) and by the University of Alcala (under project CCG2015/EXP-056).
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García-Gómez, J., Bautista-Durán, M., Gil-Pita, R., Mohino-Herranz, I., Rosa-Zurera, M. (2016). Violence Detection in Real Environments for Smart Cities. In: García, C., Caballero-Gil, P., Burmester, M., Quesada-Arencibia, A. (eds) Ubiquitous Computing and Ambient Intelligence. IWAAL AmIHEALTH UCAmI 2016 2016 2016. Lecture Notes in Computer Science(), vol 10070. Springer, Cham. https://doi.org/10.1007/978-3-319-48799-1_52
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DOI: https://doi.org/10.1007/978-3-319-48799-1_52
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