Statistical analysis of extreme values
Statistical analysis of extreme values
Statistical methods for speech recognition
Statistical methods for speech recognition
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Multi-Modal Dialog Scene Detection Using Hidden Markov Models for Content-Based Multimedia Indexing
Multimedia Tools and Applications
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Audio-visual synchrony for detection of monologues in video archives
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Comparative analysis of hidden Markov models for multi-modal dialogue scene indexing
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 04
Dialogue sequence detection in movies
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
A framework for dialogue detection in movies
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
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A novel framework for audio-assisted dialogue detection based on indicator functions and neural networks is investigated. An indicator function defines that an actor is present at a particular time instant. The cross-correlation function of a pair of indicator functions and the magnitude of the corresponding cross-power spectral density are fed as input to neural networks for dialogue detection. Several types of artificial neural networks, including multilayer perceptrons (MLPs), voted perceptrons, radial basis function networks, support vector machines, and particle swarm optimization-based MLPs are tested. Experiments are carried out to validate the feasibility of the aforementioned approach by using ground-truth indicator functions determined by human observers on six different movies. A total of 41 dialogue instances and another 20 non-dialogue instances are employed. The average detection accuracy achieved is high, ranging between 84.78%+/-5.499% and 91.43%+/-4.239%.