Abstract
A Multi-agent system (MAS) which is a collection of agents cooperating with each other in order to fulfill common and individual goals can be used to implement a multimedia system for TV commercial identification. In this paper the effective autonomous method of a single commercial extracting from a advertising block and its recognition using only the audio signal based on MAS model is presented. Proposed solution uses a multidimensional orthogonal audio signal representation for a track parametrization and gives a recognition at the level of 98%.
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References
Kim, D.S.: Design and Implementation of Intelligent Agent System of Pattern Classification. Korea fuzzy logic and intelligent society 11(7), 598–602 (2001)
Kim, D.S., Kim, C.S., Rim, K.W.: Modeling and Designin of Intelligent Agent System. International Journal of Control, Automation and Systems 1(2) (June 2003)
Hakamura, Y., Nagao, M.: Parallel Feature Extraction System with Multi Agents. In: Proceedings of 11th IAPR Conference B: Pattern Recognition Methodology and Systems, Netherlands, vol. II (1992)
Kay, S.M.: Modern Spectral Estimation. Prentice Hall, Englewood Cliffs (1988)
Rabiner, L.: Fundamentals of Speech Recognition. Prentice Hall PTR, Englewood Cliffs (1993)
Lienhart, R., Kuhmunch, C., Euelsberg, W.: On the detection and recognition of television commercials. In: Proc. IEEE Conf. on Multimedia Computing and Systems, Ottawa, Canada, pp. 509–516 (June 1997)
Zabih, R., Miller, J., Mai, K.: A feature-based algorithm for detecting and classifying scene breaks. In: ACM Conference on Multimedia, San Francisco, California (November 1995)
Biernacki, P., Zarzycki, J.: Orthogonal Schur-Type Solution of the Nonlinear Noise-Cancelling Problem. In: Proc. Int. Conf. On Signals and Electronic Systems (ICSES 2000), Ustron, pp. 337–342 (2000)
Lee, D.T.L., Morf, M., Friedlander, B.: Recursive Least-Squares Ladder Estimation Algorithms. IEEE Trans. on CAS 28, 467–481 (1981)
Haitsma, J., Kalker, T., Oostveen, J.: Robust audio hashing for content identification. In: Proc. of the Content-Based Multimedia Indexing, Firenze, Italy (September 2001)
Morgan, N., Bourlard, H., Hermansky, H.: Automatic Speech Recognition: An Auditory Perspective. In: Greenberg, S., Ainsworth, W.A. (eds.) Speech Processing in the Auditory System, p. 315. Springer, Heidelberg (2004); ISBN 9780387005904
Paliwal, K.K.: Spectral subband centroid features for speech recognition. In: Proc. IEEE ICASSP, pp. 617–620 (1998)
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Biernacki, P. (2011). Application of Multi-Agents in TV Commercial Recognition System. In: O’Shea, J., Nguyen, N.T., Crockett, K., Howlett, R.J., Jain, L.C. (eds) Agent and Multi-Agent Systems: Technologies and Applications. KES-AMSTA 2011. Lecture Notes in Computer Science(), vol 6682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22000-5_42
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DOI: https://doi.org/10.1007/978-3-642-22000-5_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21999-3
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