Abstract
This work studies the problem of violence detection in audio data, which can be used for automated content rating. We employ some popular frame-level audio features both from the time and frequency domain. Afterwards, several statistics of the calculated feature sequences are fed as input to a Support Vector Machine classifier, which decides about the segment content with respect to violence. The presented experimental results verify the validity of the approach and exhibit a better performance than the other known approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Vasconcelos, N., Lippman, A.: Towards semantically meaningful feature spaces for the characterization of video content. In: Proc. International Conference on Image Processing, October 1997, vol. 1, pp. 25–28 (1997)
Datta, A., Shah, M., Lobo, N.V.: Person-on-Person Violence Detection in Video Data. In: IEEE International Conference on Pattern Recognition, Canada (2002)
Nam, J., Tewfik, A.H.: Event-driven video abstraction and visualisation. Multimedia Tools and Applications 16(1-2), 55–77 (2002)
Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 3rd edn. Academic Press, London (2005)
Cristianini, N., Shawe-Taylor, J.: Support Vector Machines and other kernel-based learning methods. Cambridge University Press, Cambridge (2000); ISBN 0-521-78019-5
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Giannakopoulos, T., Kosmopoulos, D., Aristidou, A., Theodoridis, S. (2006). Violence Content Classification Using Audio Features. In: Antoniou, G., Potamias, G., Spyropoulos, C., Plexousakis, D. (eds) Advances in Artificial Intelligence. SETN 2006. Lecture Notes in Computer Science(), vol 3955. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11752912_55
Download citation
DOI: https://doi.org/10.1007/11752912_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34117-8
Online ISBN: 978-3-540-34118-5
eBook Packages: Computer ScienceComputer Science (R0)