Abstract
When applied to interactive seminars, the detection of acoustic events from only audio information shows a large amount of errors, which are mostly due to the temporal overlaps of sounds. Video signals may be a useful additional source of information to cope with that problem for particular events. In this work, we aim at improving the detection of steps by using two audio-based Acoustic Event Detection (AED) systems, with SVM and HMM, and a video-based AED system, which employs the output of a 3D video tracking algorithm. The fuzzy integral is used to fuse the outputs of the three detection systems. Experimental results using the CLEAR 2007 evaluation data show that video information can be successfully used to improve the results of audio-based AED.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Temko, A., Malkin, R., Zieger, C., Macho, D., Nadeu, C., Omologo, M.: CLEAR Evaluation of Acoustic Event Detection and Classification systems. In: Stiefelhagen, R., Garofolo, J.S. (eds.) CLEAR 2006. LNCS, vol. 4122. Springer, Heidelberg (2007)
Zhou, X., Zhuang, X., Lui, M., Tang, H., Hasgeawa-Johnson, M., Huang, T.: HMM-Based Acoustic Event Detection with AdaBoost Feature Selection. In: Stiefelhagen, R., Bowers, R., Fiscus, J.G. (eds.) CLEAR 2007 and RT 2007. LNCS, vol. 4625, Springer, Heidelberg (2007)
Temko, A., Nadeu, C., Biel, J.-I.: Acoustic Event Detection: SVM-based System and Evaluation Setup in CLEAR 2007. In: Multimodal Technologies for Perception of Humans. LNCS, vol. 4625, pp. 354–363. Springer, Heidelberg (2008)
Kuncheva, L.: Combining Pattern Classifiers. John Wiley, Chichester (2004)
Temko, A., Macho, D., Nadeu, C.: Fuzzy Integral Based Information Fusion for Classification of Highly Confusable Non-Speech Sounds. Pattern Recognition 41(5), 1831–1840 (2008)
López, A., Canton-Ferrer, C., Casas, J.R.: Multi-Person 3D Tracking with Particle Filters on Voxels. In: IEEE ICASSP 2007, pp. 913–916 (2007)
Arulampalam., M., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transaction on Signal Processing 50, 174–188 (2002)
Lanz, O.: Approximate Bayesian Multibody Tracking. IEEE Transaction on Pattern Analysis and Machine Intelligence 28(9), 1439–1449 (2006)
Khan, Z., Balch, T., Dellaert, F.: Efficient particle filter-based tracking of multiple interacting targets using an MRF-based motion model. In: International Conference on Intelligent Robots and Systems (2003)
Hsu, C., Lin, C.: A Comparison of Methods for Multi-class Support Vector Machines. IEEE Transactions on Neural Networks, 415–425 (2002)
Young, S.J., Evermann, G., Kershaw, D., Moore, G., Odell, J., Ollason, D., Povey, D., Valtchev, V., Woodland, P.: The HTK Book (for HTK Version 3.2). Cambridge University Press, Cambridge (2002)
Shalabi, L., Shaaban, Z., Kasasbeh, B.: Data Mining: A Preprocessing Engine. Journal of Computer Science 2(9), 735–739 (2006)
Grabisch, M.: A new algorithm for identifying fuzzy measures and its application to pattern recognition. In: IEEE International Conference on Fuzzy Systems, pp. 145–150 (1995)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Butko, T., Temko, A., Nadeu, C., Canton, C. (2008). Inclusion of Video Information for Detection of Acoustic Events Using the Fuzzy Integral. In: Popescu-Belis, A., Stiefelhagen, R. (eds) Machine Learning for Multimodal Interaction. MLMI 2008. Lecture Notes in Computer Science, vol 5237. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85853-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-540-85853-9_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85852-2
Online ISBN: 978-3-540-85853-9
eBook Packages: Computer ScienceComputer Science (R0)