A neural network approach to audio-assisted movie dialogue detection

Neurocomputing

Volumn 71, Issue 1-3, 2007, Pages 157-166

  Kotti, Margarita ^a   Benetos, Emmanouil ^a   Kotropoulos, Constantine ^a   Pitas, Ioannis ^a  

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

Author keywords

Cross correlation; Cross power spectral density; Dialogue detection; Indicator functions

Indexed keywords

CORRELATION DETECTORS; OPTIMIZATION; PATTERN RECOGNITION; RADIAL BASIS FUNCTION NETWORKS; SPECTRAL DENSITY; SUPPORT VECTOR MACHINES;

CORSS-POWER SPECTRAL DENSITY; CROSS-CORRELATION; DIALOGUE DETECTION; INDICATOR FUNCTIONS;

MULTILAYER NEURAL NETWORKS;

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUDIOVISUAL EQUIPMENT; AUTOMATIC SPEECH RECOGNITION; CONTROLLED STUDY; CORRELATION FUNCTION; FEASIBILITY STUDY; INTERMETHOD COMPARISON; PERCEPTRON; PRIORITY JOURNAL; RADIAL BASED FUNCTION; SUPPORT VECTOR MACHINE; VALIDATION PROCESS;

EID: 35648941166     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2007.08.006     Document Type: Article

References (27)

1. Alatan A.A., and Akansu A.N. Multi-modal dialog scene detection using hidden-Markov models for content-based multimedia indexing. J. Multimedia Tools Appl. 14 (2001) 137-151
2. A.A. Alatan, A.N. Akansu, W. Wolf, Comparative analysis of hidden Markov models for multi-modal dialogue scene indexing, in: Proceedings of the 2000 IEEE International Conference Acoustics, Speech, and Signal Processing, vol. 4, 2000, pp. 2401-2404.
3. D. Arijon, Grammar of the Film Language, Silman-James Press, 1991.
4. Birge B. PSOt-A particle swarm optimization toolbox for Matlab. Proceedings of the 2003 IEEE Swarm Intelligence Symposiums (2003) 182-186
5. R.J. Boys, D.A. Henderson, A Bayesian approach to DNA sequence segmentation, in: Proceedings of the 2004 Biometrics, vol. 60(3) September 2004, pp. 573-588.
6. L. Chen, M.T. Özsu, Rule-based extraction from video, in: Proceedings of the IEEE International Conference on Image Processing, vol. II, 2002, pp. 737-740.
7. R. Eberhart, J. Kennedy, A new optimizer using particle swarm theory, in: Proceedings 6th International Symposium on Micro Machine and Human Science, October 1995, pp. 39-43.
8. Freund Y., and Schapire R.E. Large margin classification using the perceptron algorithm. Mach. Learn. 37 3 (1999) 277-296
9. Freund Y., and Schapire R.E. A short introduction to boosting. J. Jpn. Soc. Artif. Intell. 14 5 (1999) 771-780
10. Hosmer D.W., and Lemeshow S. Applied Logistic Regression (2000), Wiley, New York
11. G. Iyengal, H.J. Nock, C. Neti, Audio-visual synchrony for detection of monologues in video archives, in: Proceedings 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. I, April 2003, pp. 329-332.
12. Jelinek F. Statistical Methods for Speech Recognition (1997), The MIT Press, Cambridge, MA
13. M. Kotti, E. Benetos, C. Kotropoulos, Automatic speaker change detection with the Bayesian information criterion using MPEG-7 features and a fusion scheme, in: Proceedings of the 2006 IEEE International Symposium on Circuits and Systems, May 2006, pp. 1856-1859.
14. Kotti M., Kotropoulos C., Ziólko B., Pitas I., and Moschou V. A framework for dialogue detection in movies. Lecture Notes in Computer Science vol. 4105 (2006), Springer, Istanbul 371-378
15. Král P., Cerisara C., and Kleckova J. Combination of classifiers for automatic recognition of dialogue acts. Proceedings of the 9th European Conference on Speech Communication and Technology (2005) 825-828
16. Lehane B., O'Connor N., and Murphy N. Dialogue scene detection in movies using low and mid-level visual features. Proceedings of the International Conference Image and Video Retrieval (2005) 286-296
17. L. Lu, H. Zhang, Speaker change detection and tracking in real-time news broadcast analysis, in: Proceedings of the 2004 IEEE International Conference Acoustics, Speech, and Signal Processing, vol. I, May 2004, pp. 741-744.
18. MPEG-7 overview (version 9), ISO/IEC JTC1/SC29/WG11 N5525 March, 2003.
19. S. Nissen, Implementation of a fast artificial neural network library (FANN), Technical Report, Department Computer Science University of Copenhagen, Denmark, October 2003.
20. Papoulis A., and Pillai S.V. Probabilities, Random Variables, and stochastic processes. fourth ed. (2002), McGraw-Hill, New York
21. Platt J. Fast training of support vector machines using sequential minimal optimization. In: Schoelkopf B., Burges C., and Smola A. (Eds). Advances in Kernel Methods-Support Learning (1999), MIT Press, Cambridge, MA
22. Reiss R.D., and Thomas M. Statistical Analysis of Extreme Values (1997), Birkhäuser, Basel, Switzerland
23. Stoica P., and Moses R.L. Introduction to Spectral Analysis (1997), Prentice-Hall, Upper Saddle River, NJ
24. Trelea I.C. The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf. Process. Lett. 85 (2003) 317-325
25. A. Vassiliou, A. Salway, D. Pitt, Formalising stories: sequences of events and state changes, in: Proceedings of the 2004 IEEE International Conference on Multimedia and Expo, vol. I, 2004, pp. 587-590.
26. Webb I. MultiBoosting: a technique for combining boosting and wagging. J. Mach. Learn. 40 2 (2000) 159-196
27. Zhai Y., Rasheed Z., and Shah M. Semantic classification of movie scenes using finite state machines. IEE Proc. Vision Image Signal Process. 152 6 (2005) 896-901