Journal article
Acoustic Signal Based Abnormal Event Detection in Indoor Environment using Multiclass Adaboost
IEEE transactions on consumer electronics, v 59(3), pp 615-622
01 Aug 2013
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
This paper addresses the problem of abnormal acoustic event detection in indoor surveillance systems related to safety and security. The proposed concept event detector determines if the acoustic state is either normal or abnormal from accumulated series of acoustic signals using MFCC and deltas coefficients as acoustic feature vectors and a multiclass Adaboost based acoustic context classifier. A novel concept of adopting an exponential criterion and weighted least square solution to boost binary weak classifiers is proposed here for performance and speed improvements over the conventional and prominent GMM based classifiers.(1)
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Details
- Title
- Acoustic Signal Based Abnormal Event Detection in Indoor Environment using Multiclass Adaboost
- Creators
- Younghyun Lee - Korea UniversityDavid K. Han - Office of Naval ResearchHanseok Ko - Korea Univ, Sch Elect Engn, Seoul 136713, South Korea
- Publication Details
- IEEE transactions on consumer electronics, v 59(3), pp 615-622
- Publisher
- IEEE
- Number of pages
- 8
- Grant note
- WR080951 / Seoul RBD Program; Seoul RBD program
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000325924900024
- Scopus ID
- 2-s2.0-84886564908
- Other Identifier
- 991021931081204721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Engineering, Electrical & Electronic
- Telecommunications