Acoustic Detection of Violence in Real and Fictional Environments

Marta Bautista-Durán, Joaquín García-Gómez, Roberto Gil-Pita, Héctor Sánchez-Hevia, Inma Mohino-Herranz, Manuel Rosa-Zurera

Abstract

Detecting violence is an important task due to the amount of people who suffer its effects daily. There is a tendency to focus the problem either in real situations or in non real ones, but both of them are useful on its own right. Until this day there has not been clear effort to try to relate both environments. In this work we try to detect violent situations on two different acoustic databases through the use of crossed information from one of them into the other. The system has been divided into three stages: feature extraction, feature selection based on genetic algorithms and classification to take a binary decision. Results focus on comparing performance loss when a database is evaluated with features selected on itself, or selection based in the other database. In general, complex classifiers tend to suffer higher losses, whereas simple classifiers, such as linear and quadratic detectors, offers less than a 10% loss in most situations.

References

  1. Chen, L.-H., Hsu, H.-W., Wang, L.-Y., and Su, C.-W. (2011). Violence detection in movies. In Computer Graphics, Imaging and Visualization (CGIV), 2011 Eighth International Conference on, pages 119-124. IEEE.
  2. Demarty, C.-H., Penet, C., Gravier, G., and Soleymani, M. (2012). The mediaeval 2012 affect task: violent scenes detection. In Working Notes Proceedings of the MediaEval 2012 Workshop.
  3. Doukas, C. N. and Maglogiannis, I. (2011). Emergency fall incidents detection in assisted living environments utilizing motion, sound, and visual perceptual components. IEEE Transactions on Information Technology in Biomedicine, 15(2):277-289.
  4. García-G ómez, J., Bautista-Durán, M., Gil-Pita, R., Mohino-Herranz, I., and Rosa-Zurera, M. (2016). Violence detection in real environments for smart cities. In Ubiquitous Computing and Ambient Intelligence: 10th International Conference, UCAmI 2016, San Bartolomé de Tirajana, Gran Canaria, Spain, November 29-December 2, 2016, Part II, pages 482-494. Springer.
  5. Giannakopoulos, T., Kosmopoulos, D., Aristidou, A., and Theodoridis, S. (2006). Violence content classification using audio features. In Hellenic Conference on Artificial Intelligence, pages 502-507. Springer.
  6. Gil-Pita, R., López-Garrido, B., and Rosa-Zurera, M. (2015). Tailored mfccs for sound environment classification in hearing aids. In Advanced Computer and Communication Engineering Technology, pages 1037-1048. Springer.
  7. Jalil, M., Butt, F. A., and Malik, A. (2013). Short-time energy, magnitude, zero crossing rate and autocorrelation measurement for discriminating voiced and unvoiced segments of speech signals. In Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), 2013 International Conference on, pages 208-212. IEEE.
  8. Krug, E. G., Mercy, J. A., Dahlberg, L. L., and Zwi, A. B. (2002). The world report on violence and health. The lancet, 360(9339):1083-1088.
  9. Mohino, I., Gil-Pita, R., and Alvarez, L. (2011). Stress detection through emotional speech analysis. Springer.
  10. Mohino, I., Goni, M., Alvarez, L., Llerena, C., and Gil-Pita, R. (2013). Detection of emotions and stress through speech analysis. Proceedings of the Signal Processing, Pattern Recognition and Application-2013, Innsbruck, Austria, pages 12-14.
  11. Nam, J., Alghoniemy, M., and Tewfik, A. H. (1998). Audiovisual content-based violent scene characterization. In Image Processing, 1998. ICIP 98. Proceedings. 1998 International Conference on, volume 1, pages 353- 357. IEEE.
  12. Tzanetakis, G. and Cook, P. (2002). Musical genre classification of audio signals. IEEE Transactions on speech and audio processing, 10(5):293-302.
  13. Xu, M., Chia, L.-T., and Jin, J. (2005). Affective content analysis in comedy and horror videos by audio emotional event detection. In 2005 IEEE International Conference on Multimedia and Expo, pages 4- pp. IEEE.
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Paper Citation


in Harvard Style

Bautista-Durán M., García-Gómez J., Gil-Pita R., Sánchez-Hevia H., Mohino-Herranz I. and Rosa-Zurera M. (2017). Acoustic Detection of Violence in Real and Fictional Environments . In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 456-462. DOI: 10.5220/0006195004560462


in Bibtex Style

@conference{icpram17,
author={Marta Bautista-Durán and Joaquín García-Gómez and Roberto Gil-Pita and Héctor Sánchez-Hevia and Inma Mohino-Herranz and Manuel Rosa-Zurera},
title={Acoustic Detection of Violence in Real and Fictional Environments},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={456-462},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006195004560462},
isbn={978-989-758-222-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Acoustic Detection of Violence in Real and Fictional Environments
SN - 978-989-758-222-6
AU - Bautista-Durán M.
AU - García-Gómez J.
AU - Gil-Pita R.
AU - Sánchez-Hevia H.
AU - Mohino-Herranz I.
AU - Rosa-Zurera M.
PY - 2017
SP - 456
EP - 462
DO - 10.5220/0006195004560462