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Dangerous sound event recognition using Support Vector Machine classifiers

Abstract

A method of recognizing events connected to danger based on their acoustic representation through Support Vector Machine classification is presented. The method proposed is particularly useful in an automatic surveillance system. The set of 28 parameters used in the classifier consists of dedicated parameters and MPEG-7 features. Methods for parameter calculation are presented, as well as a design of SVM model used for classification. The performance of the classifier was tested on a set of 372 example sounds, yielding high accuracy.

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Details

Category:
Monographic publication
Type:
rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
Title of issue:
Advances in intelligent and soft computing: Advances in multimedia and network information system technologies strony 49 - 57
Language:
English
Publication year:
2010
Bibliographic description:
Łopatka K., Żwan P., Czyżewski A.: Dangerous sound event recognition using Support Vector Machine classifiers// Advances in intelligent and soft computing: Advances in multimedia and network information system technologies/ ed. eds.Ngoc Thanh Nguyen, Aleksander Zgrzywa, Andrzej Czyżewski. Wrocław: Springer, 2010, s.49-57
DOI:
Digital Object Identifier (open in new tab) 10.1007/978-3-642-14989-4_5
Verified by:
Gdańsk University of Technology

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