Acoustic signal based abnormal event detection in indoor environment using multiclass adaboost

Younghyun Lee, David K. Han, Hanseok Ko

Research output: Contribution to journalArticle

8 Citations (Scopus)

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.

Original languageEnglish
Article number6626247
Pages (from-to)615-622
Number of pages8
JournalIEEE Transactions on Consumer Electronics
Volume59
Issue number3
DOIs
Publication statusPublished - 2013

Keywords

  • Abnormal event detection
  • acoustic signalclassification
  • context awareness
  • multiclass Adaboost

ASJC Scopus subject areas

  • Media Technology
  • Electrical and Electronic Engineering

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