| Paper: | MLSP-P2.9 | ||
| Session: | Learning Theory and Modeling | ||
| Time: | Tuesday, May 16, 16:30 - 18:30 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Sequential learning; sequential decision methods | ||
| Title: | COMPARISON OF SEQUENCE DISCRIMINANT SUPPORT VECTOR MACHINES FOR ACOUSTIC EVENT CLASSIFICATION | ||
| Authors: | Andrey Temko, Enric Monte, Climent Nadeu, Technical University of Catalonia (UPC), Spain | ||
| Abstract: | In a previously reported work, classification techniques based on Support Vector Machines (SVM) showed a good performance in the task of acoustic event classification. SVM are discriminant classifiers, but they cannot easily deal with the dynamic time structure of sounds, since they are constrained to work with fixed-length vectors. Several methods that adapt SVM to sequence processing have been reported in the literature. In this paper, they are reviewed and applied to the classification of 16 types of sounds from the meeting room environment. With our database, we have observed that the dynamic time warping kernels work well for sounds that show a temporal structure, but the best average score is obtained with the Fisher kernel. | ||
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