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Acoustic Signal Based Abnormal Event Detection in Indoor Environment using Multiclass Adaboost

Authors
Lee, YounghyunHan, David K.Ko, Hanseok
Issue Date
8월-2013
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Abnormal event detection; acoustic signal classification; multiclass Adaboost; context awareness
Citation
IEEE TRANSACTIONS ON CONSUMER ELECTRONICS, v.59, no.3, pp.615 - 622
Indexed
SCIE
SCOPUS
Journal Title
IEEE TRANSACTIONS ON CONSUMER ELECTRONICS
Volume
59
Number
3
Start Page
615
End Page
622
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/102498
DOI
10.1109/TCE.2013.6626247
ISSN
0098-3063
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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공과대학 (전기전자공학부)
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